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The objective of this work is to re-synthesize speech directly from the speech signals without using any text in a different speaker's voice. The speech signals are transformed into a sequence of acoustic subword units or virtual phones which are discovered automatically from the given speech signals in an unsupervised manner. The speech signal is initially segmented into acoustically homogeneous segments through kernel-Gram segmentation using MFCC and autoencoder bottleneck features. These segments are then clustered using different clustering techniques. The cluster labels thus obtained are considered as virtual phone units which are used to transcribe the speech signals. The virtual phones for the utterances to be resynthesized are encoded as one-hot vector sequences. Deep neural network based duration model and acoustic model are trained for synthesis using these sequences. A vocoder is used to synthesize speech in target speaker's voice from the features estimated by the acoustic model. The performance evaluation is done on ZeroSpeech 2019 challenge on English and Indonesian language. The bitrate and speaker similarity were found to be better than the challenge baseline with slightly lower intelligibility due to the compact encoding.

This paper presents a new approach for unsupervised segmentation and labeling of acoustically homogeneous segments from the speech signals. The virtual labels, thus obtained, are used to build unsupervised acoustic models in the absence of manual transcriptions. We refer to this approach as unsupervised speech signal-to-symbol transformation. This approach mainly involves three steps: (i) segmenting the speech signal into acoustically homogeneous regions, (ii) assigning consistent labels to the acoustic segments with similar characteristics and (iii) iterative modeling of the acoustic segments sharing the same label. This work focuses on improving initial segmentation and acoustic segment labeling. A new kernel-Gram matrix-based approach is proposed for segmentation. The number of segments is automatically determined using this approach, and performance comparable to the state-of-the-art algorithms is achieved. The segment labeling is formulated in a graph clustering framework. Graph clustering methods require extensive computational resources for large datasets. A new graph growing-based strategy is proposed to make the algorithm scalable. A two-stage iterative modeling is used to refine the segment boundaries and segment labels alternately. The proposed method achieves highest normalized mutual information and purity on TIMIT dataset. Quality assessment of the virtual labels is performed by building a language identification (LID) system for Indian languages. A bigram language model is built using these virtual phones. The LID system built using these virtual labels and corresponding language model performs very close to the system trained using manual labels and an i-vector-based LID system. The fusion of unsupervised LID system scores from our approach and the i-vector approach outperforms the LID system built under the supervision of manual labels by a relative margin of 31.19% demonstrating the effectiveness of unsupervised LID systems that can be at par with

This paper presents a new approach for unsupervised segmentation and labeling of acoustically homogeneous segments from the speech signals. The virtual labels, thus obtained, are used to build unsupervised acoustic models in the absence of manual transcriptions. We refer to this approach as unsupervised speech signal-to-symbol transformation. This approach mainly involves three steps: (i) segmenting the speech signal into acoustically homogeneous regions, (ii) assigning consistent labels to the acoustic segments with similar characteristics and (iii) iterative modeling of the acoustic segments sharing the same label. This work focuses on improving initial segmentation and acoustic segment labeling. A new kernel-Gram matrix-based approach is proposed for segmentation. The number of segments is automatically determined using this approach, and performance comparable to the state-of-the-art algorithms is achieved. The segment labeling is formulated in a graph clustering framework. Graph clustering methods require extensive computational resources for large datasets. A new graph growing-based strategy is proposed to make the algorithm scalable. A two-stage iterative modeling is used to refine the segment boundaries and segment labels alternately. The proposed method achieves highest normalized mutual information and purity on TIMIT dataset. Quality assessment of the virtual labels is performed by building a language identification (LID) system for Indian languages. A bigram language model is built using these virtual phones. The LID system built using these virtual labels and corresponding language model performs very close to the system trained using manual labels and an i-vector-based LID system. The fusion of unsupervised LID system scores from our approach and the i-vector approach outperforms the LID system built under the supervision of manual labels by a relative margin of 31.19% demonstrating the effectiveness of unsupervised LID systems that can be at par with

This paper presents a new approach for unsupervised segmentation and labeling of acoustically homogeneous segments from the speech signals. The virtual labels, thus obtained, are used to build unsupervised acoustic models in the absence of manual transcriptions. We refer to this approach as unsupervised speech signal-to-symbol transformation. This approach mainly involves three steps: (i) segmenting the speech signal into acoustically homogeneous regions, (ii) assigning consistent labels to the acoustic segments with similar characteristics and (iii) iterative modeling of the acoustic segments sharing the same label. This work focuses on improving initial segmentation and acoustic segment labeling. A new kernel-Gram matrix-based approach is proposed for segmentation. The number of segments is automatically determined using this approach, and performance comparable to the state-of-the-art algorithms is achieved. The segment labeling is formulated in a graph clustering framework. Graph clustering methods require extensive computational resources for large datasets. A new graph growing-based strategy is proposed to make the algorithm scalable. A two-stage iterative modeling is used to refine the segment boundaries and segment labels alternately. The proposed method achieves highest normalized mutual information and purity on TIMIT dataset. Quality assessment of the virtual labels is performed by building a language identification (LID) system for Indian languages. A bigram language model is built using these virtual phones. The LID system built using these virtual labels and corresponding language model performs very close to the system trained using manual labels and an i-vector-based LID system. The fusion of unsupervised LID system scores from our approach and the i-vector approach outperforms the LID system built under the supervision of manual labels by a relative margin of 31.19% demonstrating the effectiveness of unsupervised LID systems that can be at par with

This paper presents a new approach for unsupervised segmentation and labeling of acoustically homogeneous segments from the speech signals. The virtual labels, thus obtained, are used to build unsupervised acoustic models in the absence of manual transcriptions. We refer to this approach as unsupervised speech signal-to-symbol transformation. This approach mainly involves three steps: (i) segmenting the speech signal into acoustically homogeneous regions, (ii) assigning consistent labels to the acoustic segments with similar characteristics and (iii) iterative modeling of the acoustic segments sharing the same label. This work focuses on improving initial segmentation and acoustic segment labeling. A new kernel-Gram matrix-based approach is proposed for segmentation. The number of segments is automatically determined using this approach, and performance comparable to the state-of-the-art algorithms is achieved. The segment labeling is formulated in a graph clustering framework. Graph clustering methods require extensive computational resources for large datasets. A new graph growing-based strategy is proposed to make the algorithm scalable. A two-stage iterative modeling is used to refine the segment boundaries and segment labels alternately. The proposed method achieves highest normalized mutual information and purity on TIMIT dataset. Quality assessment of the virtual labels is performed by building a language identification (LID) system for Indian languages. A bigram language model is built using these virtual phones. The LID system built using these virtual labels and corresponding language model performs very close to the system trained using manual labels and an i-vector-based LID system. The fusion of unsupervised LID system scores from our approach and the i-vector approach outperforms the LID system built under the supervision of manual labels by a relative margin of 31.19% demonstrating the effectiveness of unsupervised LID systems that can be at par with

This paper presents a new approach for unsupervised segmentation and labeling of acoustically homogeneous segments from the speech signals. The virtual labels, thus obtained, are used to build unsupervised acoustic models in the absence of manual transcriptions. We refer to this approach as unsupervised speech signal-to-symbol transformation. This approach mainly involves three steps: (i) segmenting the speech signal into acoustically homogeneous regions, (ii) assigning consistent labels to the acoustic segments with similar characteristics and (iii) iterative modeling of the acoustic segments sharing the same label. This work focuses on improving initial segmentation and acoustic segment labeling. A new kernel-Gram matrix-based approach is proposed for segmentation. The number of segments is automatically determined using this approach, and performance comparable to the state-of-the-art algorithms is achieved. The segment labeling is formulated in a graph clustering framework. Graph clustering methods require extensive computational resources for large datasets. A new graph growing-based strategy is proposed to make the algorithm scalable. A two-stage iterative modeling is used to refine the segment boundaries and segment labels alternately. The proposed method achieves highest normalized mutual information and purity on TIMIT dataset. Quality assessment of the virtual labels is performed by building a language identification (LID) system for Indian languages. A bigram language model is built using these virtual phones. The LID system built using these virtual labels and corresponding language model performs very close to the system trained using manual labels and an i-vector-based LID system. The fusion of unsupervised LID system scores from our approach and the i-vector approach outperforms the LID system built under the supervision of manual labels by a relative margin of 31.19% demonstrating the effectiveness of unsupervised LID systems that can be at par with

The objective of this work is to re-synthesize speech directly from the speech signals without using any text in a different speaker's voice. The speech signals are transformed into a sequence of acoustic subword units or virtual phones which are discovered automatically from the given speech signals in an unsupervised manner. The speech signal is initially segmented into acoustically homogeneous segments through kernel-Gram segmentation using MFCC and autoencoder bottleneck features. These segments are then clustered using different clustering techniques. The cluster labels thus obtained are considered as virtual phone units which are used to transcribe the speech signals. The virtual phones for the utterances to be resynthesized are encoded as one-hot vector sequences. Deep neural network based duration model and acoustic model are trained for synthesis using these sequences. A vocoder is used to synthesize speech in target speaker's voice from the features estimated by the acoustic model. The performance evaluation is done on ZeroSpeech 2019 challenge on English and Indonesian language. The bitrate and speaker similarity were found to be better than the challenge baseline with slightly lower intelligibility due to the compact encoding.

The objective of this work is to re-synthesize speech directly from the speech signals without using any text in a different speaker's voice. The speech signals are transformed into a sequence of acoustic subword units or virtual phones which are discovered automatically from the given speech signals in an unsupervised manner. The speech signal is initially segmented into acoustically homogeneous segments through kernel-Gram segmentation using MFCC and autoencoder bottleneck features. These segments are then clustered using different clustering techniques. The cluster labels thus obtained are considered as virtual phone units which are used to transcribe the speech signals. The virtual phones for the utterances to be resynthesized are encoded as one-hot vector sequences. Deep neural network based duration model and acoustic model are trained for synthesis using these sequences. A vocoder is used to synthesize speech in target speaker's voice from the features estimated by the acoustic model. The performance evaluation is done on ZeroSpeech 2019 challenge on English and Indonesian language. The bitrate and speaker similarity were found to be better than the challenge baseline with slightly lower intelligibility due to the compact encoding.

Analytic phase of the speech signal plays an important role in human speech perception, specially in the presence of noise. Generally, phase information is ignored in most of the recent speech recognition systems. In this paper, we illustrate the importance of analytic phase of the speech signal for noise robust automatic speech recognition. To avoid phase wrapping problem involved in the computation of analytic phase, features are extracted from instantaneous frequency (IF) which is time derivative of analytic phase. Deep neural network (DNN) based acoustic models are trained on clean speech using features extracted from the IF of speech signals. Robustness of IF features in combination with mel-frequency cepstral coefficients (MFCCs) was evaluated in varied noisy conditions. System combination using minimum Bayes risk decoding of IF features with MFCCs delivered absolute improvements of upto 13% over MFCC features alone for DNN based systems under noisy conditions. The impact of the system combination of magnitude and phase based features on different phonetic classes was studied under noisy conditions and was found to model both voiced and unvoiced phonetic classes efficiently.

Speaking rate is an important attribute of the speech signal which plays a crucial role in the performance of automatic speech processing systems. In this paper, we propose to estimate the speaking rate by segmenting the speech into syllable-like units using end point detection algorithms which do not require any training and fine-tuning. Also, there are no predefined constraints on the expected number of syllabic segments. The acoustic subword units are obtained only from speech signal to estimate the speaking rate without any requirement of transcriptions or phonetic knowledge of the speech data. A recent theta-rate oscillator based syllabification algorithm is also employed for speaking rate estimation. The performance is evaluated on TIMIT corpus and spontaneous speech from Switchboard corpus. The correlation results are comparable to recent algorithms which are trained with specific training set and/or make use of the available transcriptions.

Unsupervised discovery of acoustic units from the raw speech signal forms the core objective of zero-resource speech processing. It involves identifying the acoustic segment boundaries and consistently assigning unique labels to acoustically similar segments. In this work, the possible candidates for segment boundaries are identified in an unsupervised manner from the kernel Gram matrix computed from the Mel-frequency cepstral coefficients (MFCC). These segment boundary candidates are used to train a siamese network, that is intended to learn embeddings that minimize intrasegment distances and maximize the intersegment distances. The siamese embeddings capture phonetic information from longer contexts of the speech signal and enhance the intersegment discriminability. These properties make the siamese embeddings better suited for acoustic segmentation and clustering than the raw MFCC features. The Gram matrix computed from the siamese embeddings provides unambiguous evidence for boundary locations. The initial candidate boundaries are refined using this evidence, and siamese embeddings are extracted for the new acoustic segments. A graph growing approach is used to cluster the siamese embeddings, and a unique label is assigned to acoustically similar segments. The performance of the proposed method for acoustic segmentation and clustering is evaluated on Zero Resource 2017 database.

Zero resource speech processing refers to techniques which do not require manually transcribed speech data. The inspiration for zero resource is drawn from language acquisition in infants which is completely self-driven. Infants learn di_erent abstraction levels i.e. phones, words and some syntactic aspects of the language they are exposed to, without any supervision or feedback. This motivated the research in speech community towards the development of completely unsupervised speech algorithms which can discover subword/word units from speech signal alone. The applications include spoken term discovery, language identi_cation, keyword spotting etc. Zero resource techniques can be e_ective in solving problems associated with the development of speech systems for low resource languages. Low resource languages have low amount of transcribed data and/or low number of native speakers. Several languages of the world have become endangered languages with almost negligible resources. The lack of transcribed data for low resource languages has inspired many directions to address this problem such as data augmentation, cross-lingual and multilingual techniques with limited success. In this thesis, we explore better feature representations for low resource speech recognition and later build unsupervised algorithms for zero resource speech processing which could lead to directions to e_ective solutions to the low resource problem. Traditional speech recognition systems employed magnitude based features for building acoustic models. Phase of the speech signals is generally ignored as human ear was considered traditionally to be indi_erent to phase. Recent perceptual studies have shown the importance of phase in human speech recognition. Motivated by this fact and in order to leverage the maximum information from limited transcribed data available in low resource settings, we propose to extract features from the analytic phase of speech signals for speech recognition. In order t

Unsupervised discovery of acoustic units from the raw speech signal forms the core objective of zero-resource speech processing. It involves identifying the acoustic segment boundaries and consistently assigning unique labels to acoustically similar segments. In this work, the possible candidates for segment boundaries are identified in an unsupervised manner from the kernel Gram matrix computed from the Mel-frequency cepstral coefficients (MFCC). These segment boundary candidates are used to train a siamese network, that is intended to learn embeddings that minimize intrasegment distances and maximize the intersegment distances. The siamese embeddings capture phonetic information from longer contexts of the speech signal and enhance the intersegment discriminability. These properties make the siamese embeddings better suited for acoustic segmentation and clustering than the raw MFCC features. The Gram matrix computed from the siamese embeddings provides unambiguous evidence for boundary locations. The initial candidate boundaries are refined using this evidence, and siamese embeddings are extracted for the new acoustic segments. A graph growing approach is used to cluster the siamese embeddings, and a unique label is assigned to acoustically similar segments. The performance of the proposed method for acoustic segmentation and clustering is evaluated on Zero Resource 2017 database.

Unsupervised discovery of acoustic units from the raw speech signal forms the core objective of zero-resource speech processing. It involves identifying the acoustic segment boundaries and consistently assigning unique labels to acoustically similar segments. In this work, the possible candidates for segment boundaries are identified in an unsupervised manner from the kernel Gram matrix computed from the Mel-frequency cepstral coefficients (MFCC). These segment boundary candidates are used to train a siamese network, that is intended to learn embeddings that minimize intrasegment distances and maximize the intersegment distances. The siamese embeddings capture phonetic information from longer contexts of the speech signal and enhance the intersegment discriminability. These properties make the siamese embeddings better suited for acoustic segmentation and clustering than the raw MFCC features. The Gram matrix computed from the siamese embeddings provides unambiguous evidence for boundary locations. The initial candidate boundaries are refined using this evidence, and siamese embeddings are extracted for the new acoustic segments. A graph growing approach is used to cluster the siamese embeddings, and a unique label is assigned to acoustically similar segments. The performance of the proposed method for acoustic segmentation and clustering is evaluated on Zero Resource 2017 database.

Analytic phase of the speech signal plays an important role in human speech perception, specially in the presence of noise. Generally, phase information is ignored in most of the recent speech recognition systems. In this paper, we illustrate the importance of analytic phase of the speech signal for noise robust automatic speech recognition. To avoid phase wrapping problem involved in the computation of analytic phase, features are extracted from instantaneous frequency (IF) which is time derivative of analytic phase. Deep neural network (DNN) based acoustic models are trained on clean speech using features extracted from the IF of speech signals. Robustness of IF features in combination with mel-frequency cepstral coefficients (MFCCs) was evaluated in varied noisy conditions. System combination using minimum Bayes risk decoding of IF features with MFCCs delivered absolute improvements of upto 13% over MFCC features alone for DNN based systems under noisy conditions. The impact of the system combination of magnitude and phase based features on different phonetic classes was studied under noisy conditions and was found to model both voiced and unvoiced phonetic classes efficiently.

Speaking rate is an important attribute of the speech signal which plays a crucial role in the performance of automatic speech processing systems. In this paper, we propose to estimate the speaking rate by segmenting the speech into syllable-like units using end point detection algorithms which do not require any training and fine-tuning. Also, there are no predefined constraints on the expected number of syllabic segments. The acoustic subword units are obtained only from speech signal to estimate the speaking rate without any requirement of transcriptions or phonetic knowledge of the speech data. A recent theta-rate oscillator based syllabification algorithm is also employed for speaking rate estimation. The performance is evaluated on TIMIT corpus and spontaneous speech from Switchboard corpus. The correlation results are comparable to recent algorithms which are trained with specific training set and/or make use of the available transcriptions.

Speaking rate is an important attribute of the speech signal which plays a crucial role in the performance of automatic speech processing systems. In this paper, we propose to estimate the speaking rate by segmenting the speech into syllable-like units using end point detection algorithms which do not require any training and fine-tuning. Also, there are no predefined constraints on the expected number of syllabic segments. The acoustic subword units are obtained only from speech signal to estimate the speaking rate without any requirement of transcriptions or phonetic knowledge of the speech data. A recent theta-rate oscillator based syllabification algorithm is also employed for speaking rate estimation. The performance is evaluated on TIMIT corpus and spontaneous speech from Switchboard corpus. The correlation results are comparable to recent algorithms which are trained with specific training set and/or make use of the available transcriptions.

Analytic phase of the speech signal plays an important role in human speech perception, specially in the presence of noise. Generally, phase information is ignored in most of the recent speech recognition systems. In this paper, we illustrate the importance of analytic phase of the speech signal for noise robust automatic speech recognition. To avoid phase wrapping problem involved in the computation of analytic phase, features are extracted from instantaneous frequency (IF) which is time derivative of analytic phase. Deep neural network (DNN) based acoustic models are trained on clean speech using features extracted from the IF of speech signals. Robustness of IF features in combination with mel-frequency cepstral coefficients (MFCCs) was evaluated in varied noisy conditions. System combination using minimum Bayes risk decoding of IF features with MFCCs delivered absolute improvements of upto 13% over MFCC features alone for DNN based systems under noisy conditions. The impact of the system combination of magnitude and phase based features on different phonetic classes was studied under noisy conditions and was found to model both voiced and unvoiced phonetic classes efficiently.

The objective of this paper is to develop an unsupervised method for segmentation of speech signals into phoneme-like units. The proposed algorithm is based on the observation that the feature vectors from the same segment exhibit higher degree of similarity than the feature vectors across the segments. The kernel-Gram matrix of an utterance is formed by computing the similarity between every pair of feature vectors in the Gaussian kernel space. The kernel-Gram matrix consists of square patches, along with the principle diagonal, corresponding to different phoneme-like segments in the speech signal. It detects the number of segments, as well as their boundaries automatically. The proposed approach does not assume any information about input utterances like exact distribution of segment length or correct number of segments in an utterance. The proposed method out-performs the state-of-the-art blind segmentation algorithms on Zero Resource 2015 databases and TIMIT database.

The objective of this paper is to develop an unsupervised method for segmentation of speech signals into phoneme-like units. The proposed algorithm is based on the observation that the feature vectors from the same segment exhibit higher degree of similarity than the feature vectors across the segments. The kernel-Gram matrix of an utterance is formed by computing the similarity between every pair of feature vectors in the Gaussian kernel space. The kernel-Gram matrix consists of square patches, along with the principle diagonal, corresponding to different phoneme-like segments in the speech signal. It detects the number of segments, as well as their boundaries automatically. The proposed approach does not assume any information about input utterances like exact distribution of segment length or correct number of segments in an utterance. The proposed method out-performs the state-of-the-art blind segmentation algorithms on Zero Resource 2015 databases and TIMIT database.

The objective of this paper is to develop an unsupervised method for segmentation of speech signals into phoneme-like units. The proposed algorithm is based on the observation that the feature vectors from the same segment exhibit higher degree of similarity than the feature vectors across the segments. The kernel-Gram matrix of an utterance is formed by computing the similarity between every pair of feature vectors in the Gaussian kernel space. The kernel-Gram matrix consists of square patches, along with the principle diagonal, corresponding to different phoneme-like segments in the speech signal. It detects the number of segments, as well as their boundaries automatically. The proposed approach does not assume any information about input utterances like exact distribution of segment length or correct number of segments in an utterance. The proposed method out-performs the state-of-the-art blind segmentation algorithms on Zero Resource 2015 databases and TIMIT database.

There have been several studies, in the recent past, pointing to the importance of analytic phase of the speech signal in human perception, especially in noisy conditions. However, phase information is still not used in state-of-the-art speech recognition systems. In this paper, we illustrate the importance of analytic phase of the speech signal for automatic speech recognition. As the computation of analytic phase suffers from inevitable phase wrapping problem, we extract features from its time derivative, referred to as instantaneous frequency (IF). In this work, we highlight the issues involved in IF extraction from speech-like signals, and propose suitable modifications for IF extraction from speech signals. We used the deep neural network (DNN) framework to build a speech recognition system using features extracted from the IF of speech signals. The speech recognition system based on IF features delivered a phoneme error rate of 21.8% on TIMIT database, while the baseline system based on mel-frequency cepstral coefficients (MFCCs) delivered a phoneme error rate of 18.4%. The combination of IF and MFCC features based systems, using minimum Bayes risk (MBR) decoding, provided a relative improvement of 8.7% over the baseline system, illustrating the significance of analytic phase for speech recognition.

The objective of this paper is to extract robust features for detecting replay spoof attacks on text-independent speaker verification systems. In the case of replay attacks, prerecorded utterance of the target speaker is played to automatic speaker verification (ASV) system to gain unauthorized access. In such a scenario, the speech signal carries the characteristics of the intermediate recording device as well. In the proposed approach, the characteristics of the intermediate device are highlighted by subtracting the contribution of the live speech in the cepstral domain. An overcomplete dictionary learned on cepstral features, extracted from live speech data, is used to subtract the contribution of live speech. The residual captures the characteristics of recording device, and can be used to distinguish spoof speech signal from live speech signal. The distribution of the residuals from live and spoof speech signals are captured using Gaussian mixture models (GMMs). The likelihood ratio computed from the GMMs built on spoof and live signals, respectively, is used to detect the spoof attack. The performance of the proposed approach is evaluated on ASVspoof 2017 evaluation challenge database. The proposed feature extraction method achieved 20.18% relative improvement over the base line system built on the constant-Q cepstral coefficients.

Zero resource speech processing refers to a scenario where no or minimal transcribed data is available. In this paper, we propose a three-step unsupervised approach to zero resource speech processing, which does not require any other information/dataset. In the first step, we segment the speech signal into phonemelike units, resulting in a large number of varying length segments. The second step involves clustering the varying-length segments into a finite number of clusters so that each segment can be labeled with a cluster index. The unsupervised transcriptions, thus obtained, can be thought of as a sequence of virtual phone labels. In the third step, a deep neural network classifier is trained to map the feature vectors extracted from the signal to its corresponding virtual phone label. The virtual phone posteriors extracted from the DNN are used as features in the zero resource speech processing. The effectiveness of the proposed approach is evaluated on both ABX and spoken term discovery tasks (STD) using spontaneous American English and Tsonga language datasets, provided as part of zero resource 2015 challenge. It is observed that the proposed system outperforms baselines, supplied along the datasets, in both the tasks without any task specific modifications.

Zero resource speech processing refers to a scenario where no or minimal transcribed data is available. In this paper, we propose a three-step unsupervised approach to zero resource speech processing, which does not require any other information/dataset. In the first step, we segment the speech signal into phonemelike units, resulting in a large number of varying length segments. The second step involves clustering the varying-length segments into a finite number of clusters so that each segment can be labeled with a cluster index. The unsupervised transcriptions, thus obtained, can be thought of as a sequence of virtual phone labels. In the third step, a deep neural network classifier is trained to map the feature vectors extracted from the signal to its corresponding virtual phone label. The virtual phone posteriors extracted from the DNN are used as features in the zero resource speech processing. The effectiveness of the proposed approach is evaluated on both ABX and spoken term discovery tasks (STD) using spontaneous American English and Tsonga language datasets, provided as part of zero resource 2015 challenge. It is observed that the proposed system outperforms baselines, supplied along the datasets, in both the tasks without any task specific modifications

Zero resource speech processing refers to a scenario where no or minimal transcribed data is available. In this paper, we propose a three-step unsupervised approach to zero resource speech processing, which does not require any other information/dataset. In the first step, we segment the speech signal into phonemelike units, resulting in a large number of varying length segments. The second step involves clustering the varying-length segments into a finite number of clusters so that each segment can be labeled with a cluster index. The unsupervised transcriptions, thus obtained, can be thought of as a sequence of virtual phone labels. In the third step, a deep neural network classifier is trained to map the feature vectors extracted from the signal to its corresponding virtual phone label. The virtual phone posteriors extracted from the DNN are used as features in the zero resource speech processing. The effectiveness of the proposed approach is evaluated on both ABX and spoken term discovery tasks (STD) using spontaneous American English and Tsonga language datasets, provided as part of zero resource 2015 challenge. It is observed that the proposed system outperforms baselines, supplied along the datasets, in both the tasks without any task specific modifications.

Zero resource speech processing refers to a scenario where no or minimal transcribed data is available. In this paper, we propose a three-step unsupervised approach to zero resource speech processing, which does not require any other information/dataset. In the first step, we segment the speech signal into phonemelike units, resulting in a large number of varying length segments. The second step involves clustering the varying-length segments into a finite number of clusters so that each segment can be labeled with a cluster index. The unsupervised transcriptions, thus obtained, can be thought of as a sequence of virtual phone labels. In the third step, a deep neural network classifier is trained to map the feature vectors extracted from the signal to its corresponding virtual phone label. The virtual phone posteriors extracted from the DNN are used as features in the zero resource speech processing. The effectiveness of the proposed approach is evaluated on both ABX and spoken term discovery tasks (STD) using spontaneous American English and Tsonga language datasets, provided as part of zero resource 2015 challenge. It is observed that the proposed system outperforms baselines, supplied along the datasets, in both the tasks without any task specific modifications.

Zero resource speech processing refers to a scenario where no or minimal transcribed data is available. In this paper, we propose a three-step unsupervised approach to zero resource speech processing, which does not require any other information/dataset. In the first step, we segment the speech signal into phonemelike units, resulting in a large number of varying length segments. The second step involves clustering the varying-length segments into a finite number of clusters so that each segment can be labeled with a cluster index. The unsupervised transcriptions, thus obtained, can be thought of as a sequence of virtual phone labels. In the third step, a deep neural network classifier is trained to map the feature vectors extracted from the signal to its corresponding virtual phone label. The virtual phone posteriors extracted from the DNN are used as features in the zero resource speech processing. The effectiveness of the proposed approach is evaluated on both ABX and spoken term discovery tasks (STD) using spontaneous American English and Tsonga language datasets, provided as part of zero resource 2015 challenge. It is observed that the proposed system outperforms baselines, supplied along the datasets, in both the tasks without any task specific modifications.

Zero resource speech processing refers to a scenario where no or minimal transcribed data is available. In this paper, we propose a three-step unsupervised approach to zero resource speech processing, which does not require any other information/dataset. In the first step, we segment the speech signal into phonemelike units, resulting in a large number of varying length segments. The second step involves clustering the varying-length segments into a finite number of clusters so that each segment can be labeled with a cluster index. The unsupervised transcriptions, thus obtained, can be thought of as a sequence of virtual phone labels. In the third step, a deep neural network classifier is trained to map the feature vectors extracted from the signal to its corresponding virtual phone label. The virtual phone posteriors extracted from the DNN are used as features in the zero resource speech processing. The effectiveness of the proposed approach is evaluated on both ABX and spoken term discovery tasks (STD) using spontaneous American English and Tsonga language datasets, provided as part of zero resource 2015 challenge. It is observed that the proposed system outperforms baselines, supplied along the datasets, in both the tasks without any task specific modifications.

Zero resource speech processing refers to a scenario where no or minimal transcribed data is available. In this paper, we propose a three-step unsupervised approach to zero resource speech processing, which does not require any other information/dataset. In the first step, we segment the speech signal into phonemelike units, resulting in a large number of varying length segments. The second step involves clustering the varying-length segments into a finite number of clusters so that each segment can be labeled with a cluster index. The unsupervised transcriptions, thus obtained, can be thought of as a sequence of virtual phone labels. In the third step, a deep neural network classifier is trained to map the feature vectors extracted from the signal to its corresponding virtual phone label. The virtual phone posteriors extracted from the DNN are used as features in the zero resource speech processing. The effectiveness of the proposed approach is evaluated on both ABX and spoken term discovery tasks (STD) using spontaneous American English and Tsonga language datasets, provided as part of zero resource 2015 challenge. It is observed that the proposed system outperforms baselines, supplied along the datasets, in both the tasks without any task specific modifications.

Zero resource speech processing refers to a scenario where no or minimal transcribed data is available. In this paper, we propose a three-step unsupervised approach to zero resource speech processing, which does not require any other information/dataset. In the first step, we segment the speech signal into phonemelike units, resulting in a large number of varying length segments. The second step involves clustering the varying-length segments into a finite number of clusters so that each segment can be labeled with a cluster index. The unsupervised transcriptions, thus obtained, can be thought of as a sequence of virtual phone labels. In the third step, a deep neural network classifier is trained to map the feature vectors extracted from the signal to its corresponding virtual phone label. The virtual phone posteriors extracted from the DNN are used as features in the zero resource speech processing. The effectiveness of the proposed approach is evaluated on both ABX and spoken term discovery tasks (STD) using spontaneous American English and Tsonga language datasets, provided as part of zero resource 2015 challenge. It is observed that the proposed system outperforms baselines, supplied along the datasets, in both the tasks without any task specific modifications.

Zero resource speech processing refers to a scenario where no or minimal transcribed data is available. In this paper, we propose a three-step unsupervised approach to zero resource speech processing, which does not require any other information/dataset. In the first step, we segment the speech signal into phonemelike units, resulting in a large number of varying length segments. The second step involves clustering the varying-length segments into a finite number of clusters so that each segment can be labeled with a cluster index. The unsupervised transcriptions, thus obtained, can be thought of as a sequence of virtual phone labels. In the third step, a deep neural network classifier is trained to map the feature vectors extracted from the signal to its corresponding virtual phone label. The virtual phone posteriors extracted from the DNN are used as features in the zero resource speech processing. The effectiveness of the proposed approach is evaluated on both ABX and spoken term discovery tasks (STD) using spontaneous American English and Tsonga language datasets, provided as part of zero resource 2015 challenge. It is observed that the proposed system outperforms baselines, supplied along the datasets, in both the tasks without any task specific modifications.

Zero resource speech processing refers to a scenario where no or minimal transcribed data is available. In this paper, we propose a three-step unsupervised approach to zero resource speech processing, which does not require any other information/dataset. In the first step, we segment the speech signal into phonemelike units, resulting in a large number of varying length segments. The second step involves clustering the varying-length segments into a finite number of clusters so that each segment can be labeled with a cluster index. The unsupervised transcriptions, thus obtained, can be thought of as a sequence of virtual phone labels. In the third step, a deep neural network classifier is trained to map the feature vectors extracted from the signal to its corresponding virtual phone label. The virtual phone posteriors extracted from the DNN are used as features in the zero resource speech processing. The effectiveness of the proposed approach is evaluated on both ABX and spoken term discovery tasks (STD) using spontaneous American English and Tsonga language datasets, provided as part of zero resource 2015 challenge. It is observed that the proposed system outperforms baselines, supplied along the datasets, in both the tasks without any task specific modifications.

Zero resource speech processing refers to a scenario where no or minimal transcribed data is available. In this paper, we propose a three-step unsupervised approach to zero resource speech processing, which does not require any other information/dataset. In the first step, we segment the speech signal into phonemelike units, resulting in a large number of varying length segments. The second step involves clustering the varying-length segments into a finite number of clusters so that each segment can be labeled with a cluster index. The unsupervised transcriptions, thus obtained, can be thought of as a sequence of virtual phone labels. In the third step, a deep neural network classifier is trained to map the feature vectors extracted from the signal to its corresponding virtual phone label. The virtual phone posteriors extracted from the DNN are used as features in the zero resource speech processing. The effectiveness of the proposed approach is evaluated on both ABX and spoken term discovery tasks (STD) using spontaneous American English and Tsonga language datasets, provided as part of zero resource 2015 challenge. It is observed that the proposed system outperforms baselines, supplied along the datasets, in both the tasks without any task specific modifications

The objective of this paper is to extract robust features for detecting replay spoof attacks on text-independent speaker verification systems. In the case of replay attacks, prerecorded utterance of the target speaker is played to automatic speaker verification (ASV) system to gain unauthorized access. In such a scenario, the speech signal carries the characteristics of the intermediate recording device as well. In the proposed approach, the characteristics of the intermediate device are highlighted by subtracting the contribution of the live speech in the cepstral domain. An overcomplete dictionary learned on cepstral features, extracted from live speech data, is used to subtract the contribution of live speech. The residual captures the characteristics of recording device, and can be used to distinguish spoof speech signal from live speech signal. The distribution of the residuals from live and spoof speech signals are captured using Gaussian mixture models (GMMs). The likelihood ratio computed from the GMMs built on spoof and live signals, respectively, is used to detect the spoof attack. The performance of the proposed approach is evaluated on ASVspoof 2017 evaluation challenge database. The proposed feature extraction method achieved 20.18% relative improvement over the base line system built on the constant-Q cepstral coefficients.

There have been several studies, in the recent past, pointing to the importance of analytic phase of the speech signal in human perception, especially in noisy conditions. However, phase information is still not used in state-of-the-art speech recognition systems. In this paper, we illustrate the importance of analytic phase of the speech signal for automatic speech recognition. As the computation of analytic phase suffers from inevitable phase wrapping problem, we extract features from its time derivative, referred to as instantaneous frequency (IF). In this work, we highlight the issues involved in IF extraction from speech-like signals, and propose suitable modifications for IF extraction from speech signals. We used the deep neural network (DNN) framework to build a speech recognition system using features extracted from the IF of speech signals. The speech recognition system based on IF features delivered a phoneme error rate of 21.8% on TIMIT database, while the baseline system based on mel-frequency cepstral coefficients (MFCCs) delivered a phoneme error rate of 18.4%. The combination of IF and MFCC features based systems, using minimum Bayes risk (MBR) decoding, provided a relative improvement of 8.7% over the baseline system, illustrating the significance of analytic phase for speech recognition.

The objective of this paper is to extract robust features for detecting replay spoof attacks on text-independent speaker verification systems. In the case of replay attacks, prerecorded utterance of the target speaker is played to automatic speaker verification (ASV) system to gain unauthorized access. In such a scenario, the speech signal carries the characteristics of the intermediate recording device as well. In the proposed approach, the characteristics of the intermediate device are highlighted by subtracting the contribution of the live speech in the cepstral domain. An overcomplete dictionary learned on cepstral features, extracted from live speech data, is used to subtract the contribution of live speech. The residual captures the characteristics of recording device, and can be used to distinguish spoof speech signal from live speech signal. The distribution of the residuals from live and spoof speech signals are captured using Gaussian mixture models (GMMs). The likelihood ratio computed from the GMMs built on spoof and live signals, respectively, is used to detect the spoof attack. The performance of the proposed approach is evaluated on ASVspoof 2017 evaluation challenge database. The proposed feature extraction method achieved 20.18% relative improvement over the base line system built on the constant-Q cepstral coefficients.

There have been several studies, in the recent past, pointing to the importance of analytic phase of the speech signal in human perception, especially in noisy conditions. However, phase information is still not used in state-of-the-art speech recognition systems. In this paper, we illustrate the importance of analytic phase of the speech signal for automatic speech recognition. As the computation of analytic phase suffers from inevitable phase wrapping problem, we extract features from its time derivative, referred to as instantaneous frequency (IF). In this work, we highlight the issues involved in IF extraction from speech-like signals, and propose suitable modifications for IF extraction from speech signals. We used the deep neural network (DNN) framework to build a speech recognition system using features extracted from the IF of speech signals. The speech recognition system based on IF features delivered a phoneme error rate of 21.8% on TIMIT database, while the baseline system based on mel-frequency cepstral coefficients (MFCCs) delivered a phoneme error rate of 18.4%. The combination of IF and MFCC features based systems, using minimum Bayes risk (MBR) decoding, provided a relative improvement of 8.7% over the baseline system, illustrating the significance of analytic phase for speech recognition.