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Adaptive multi-rate (AMR), a popular audio compression standard, is widely used in mobile communication and mobile Internet applications and has become a novel carrier for hiding information. To improve the statistical security, this paper presents a steganographic scheme in the AMR fixed codebook (FCB) domain based on the pulse distribution model (PDM-AFS), which is obtained from the distribution characteristics of the FCB value in the cover audio. The pulse positions in stego audio are controlled by message encoding and random masking to make the statistical distribution of the FCB parameters close to that of the cover audio. The experimental results show that the statistical security of the proposed scheme is better than that of the existing schemes. Furthermore, the hiding capacity is maintained compared with the existing schemes. The average hiding capacity can reach 2.06 kbps at an audio compression rate of 12.2 kbps, and the auditory concealment is good. To the best of our knowledge, this is the first secure AMR FCB steganographic scheme that improves the statistical security based on the distribution model of the cover audio. This scheme can be extended to other audio compression codecs under the principle of algebraic code excited linear prediction (ACELP), such as G.723.1 and G.729. [ABSTRACT FROM AUTHOR]

Delta Modulation (DM) is a simple waveform coding algorithm used mostly when timely data delivery is more important than the transmitted data quality. While the implementation of DM is fairly simple and inexpensive, it suffers from several limitations, such as slope overload and granular noise, which can be overcome using Adaptive Delta Modulation (ADM). This paper presents novel 2-digit ADM with six-level quantization using variable-length coding, for encoding the time-varying signals modelled by Laplacian distribution. Two variants of quantizer are employed, distortion-constrained quantizer which is optimally designed for minimal mean-squared error (MSE), and rate-constrained quantizer, which is suboptimal in the minimal MSE sense, but enables minimal loss in SQNR for the target bit rate. Experimental results using real speech signal are provided, indicating that the proposed configuration outperforms the baseline ADM algorithms, including Constant Factor Delta Modulation (CFDM), Continuously Variable Slope Delta Modulation (CVSDM), 2-digit and 2-bit ADM, and operates in a much wider dynamic range. [ABSTRACT FROM AUTHOR]

The speech coding scheme based on the simple transform coding and forward adaptive quantization for discrete input signal processing is proposed in this paper. The quasi-logarithmic quantizer is applied for discretization of continuous input signal, i.e. for preparing discrete input. The application of forward adaptation based on the input signal variance provides more efficient bandwidth usage, whereas utilization of transform coding provides sub-sequences with more predictable signal characteristics that ensure higher quality of signal reconstruction at the receiving end. In order to provide additional compression, transform coding precedes adaptive quantization. As an objective measure of system performance, signal-to-quantization-noise ratio is used. System performance is discussed for two typical cases. In the first case, it was considered that the information about continuous signal variance is available, whereas the second case considers system performance estimation when only the information about discretized signal variance is present, which means that there is a loss of input signal information. The main goal of such performance estimation comparison of the proposed speech signal coding model is to explore what is the objectivity of performance if the information about a continuous source is absent, which is a common phenomenon in digital systems. The advantages of the proposed coding scheme are demonstrated by comparing the performance of the reconstructed signal with other similar exiting speech signal coding systems. [ABSTRACT FROM AUTHOR]

Speech recognition was measured in 24 normal-hearing subjects for unprocessed speech and for speech processed by a cochlear implant Advanced Combination Encoder (ACE) coding strategy in quiet and at various signal-to noise ratios (SNRs). All signals were low- or high-pass filtered to avoid ceiling effects. Surprisingly, speech recognition performance plateaus at approximately 22 dB SNR for both speech types, implying that ACE processing has no effect on the upper limit of the effective SNR range. Speech recognition improved significantly above 15 dB SNR, suggesting that the upper limit used in the Speech Intelligibility Index should be reconsidered. [ABSTRACT FROM AUTHOR]

Many developmental studies attribute reduction of acoustic variability to increasing motor control. However, linear prediction-based formant measurements are known to be biased toward the nearest harmonic of F0, especially at high F0s. Thus, the amount of reported formant variability generated by changes in F0 is unknown. Here, 470 000 vowels were synthesized, mimicking statistics reported in four developmental studies, to estimate the proportion of formant variability that can be attributed to F0 bias, as well as other formant measurement errors. Results showed that the F0-induced formant measurements errors are large and systematic, and cannot be eliminated by a large sample size. [ABSTRACT FROM AUTHOR]

In this paper, a modified approach for frequency permutation of speech signal has been designed implemented and verified based on MATLAB Simulink. A separate permutation of real and imaginary parts of the frequency component has been used. A Short Time Objective Intelligibility has been used for checking the performance and residual intelligibility of this method. The result shows a positive indication as compared with classical permutation. [ABSTRACT FROM AUTHOR]

Linear prediction is extensively used in modeling, compression, coding, and generation of speech signal. Various formulations of linear prediction are available, both in time and frequency domain, which start from different assumptions but result in the same solution. In this letter, we propose a novel, generalized formulation of the optimal low-order linear prediction using the fractional (non-integer) derivatives. The proposed fractional derivative formulation allows for the definition of predictor with versatile behavior based on the order of fractional derivative. We derive the closed-form expressions of the optimal fractional linear predictor with restricted memory, and prove that the optimal first-order and the optimal second-order linear predictors are only its special cases. Furthermore, we empirically prove that the optimal order of fractional derivative can be approximated by the inverse of the predictor memory, and thus, it is a priori known. Therefore, the complexity is reduced by optimizing and transferring only one predictor coefficient, i.e., one parameter less in comparison to the second-order linear predictor, at the same level of performance. [ABSTRACT FROM AUTHOR]

In this paper, a novel AMR adaptive steganographic scheme based on pitch delay of unvoiced speech (PDU-AAS) was proposed. The existing AMR steganographic schemes based on pitch delay destroy the short-time relative stability of pitch delay of voiced speech segments and they are easier to be detected by the existing steganographic schemes. Especially, the pitch delay distribution of AMR voiced and unvoiced speech segments are analyzed in detail, and based on this characteristic that the pitch delay sequence of AMR unvoiced speech do not have short-term relative stability, we proposed an AMR adaptive steganographic scheme which selects the embedded position adaptively in the unvoiced speech segment, which is determined by the distribution characteristic of the adjacent pitch delay, and embeds the secret message by modifying the pitch delay without destroying the short-time stability of the pitch delay. The experiment results shows that the scheme has good concealment and hiding capability. Most important of all, the comparing experiment results show that the scheme has good security to resist the detection of the existing steganalysis algorithms. The principle of the scheme can be applied to the other steganographic schemes based on the pitch delay of the speech codec, such as G723.1, G.729. [ABSTRACT FROM AUTHOR]

Highlights • A packet-loss robust scalable wideband speech codec is proposed. • Performance is improved using the wavelet transform instead of the MDCT. • The proposed codec outperforms the state-of-the-art codec in objective tests. • High performance of the proposed codec is confirmed in subjective tests. Abstract Most recent speech codecs employ code excited linear prediction (CELP) and transmit side information to improve speech quality under packet loss. Another approach to achieve high robustness to packet loss is to use the frame independent coding scheme based on the internet low bitrate codec (iLBC). The scalable wideband speech codec based on the iLBC was previously presented and outperformed G.729.1 at most bit rates according to the objective quality. This paper presents improvements to the previous work. Specifically, we employ the wavelet packet transform (WPT) instead of the modified discrete cosine transform (MDCT) to enhance the quality, and evaluate the proposed codec based on both the objective and subjective quality measures. The objective quality evaluation results show that clear improvement is achieved and that the proposed codec outperforms G.729.1 at the bit rate of 18 kbps or higher under clean channel conditions and has higher robustness to packet loss than G.729.1. The informal subjective test results also show similar trends. [ABSTRACT FROM AUTHOR]

This paper introduces two novel algorithms for the 2-bit adaptive delta modulation, namely 2-bit hybrid adaptive delta modulation and 2-bit optimal adaptive delta modulation. In 2-bit hybrid adaptive delta modulation, the adaptation is performed both at the frame level and the sample level, where the estimated variance is used to determine the initial quantization step size. In the latter algorithm, the estimated variance is used to scale the quantizer codebook optimally designed assuming Laplace distribution of the input signal. The algorithms are tested using speech signal and compared to constant factor delta modulation, continuously variable slope delta modulation and instantaneously adaptive 2-bit delta modulation, showing that the proposed algorithms offer higher performance and significantly wider dynamic range. [ABSTRACT FROM AUTHOR]

Speech coding facilitates speech compression without perceptual loss that results in the elimination or deterioration of both speech and speaker specific features used for a wide range of applications like automatic speaker and speech recognition, biometric authentication, prosody evaluations etc. The present work investigates the effect of speech coding in the quality of features which include Mel Frequency Cepstral Coefficients, Gammatone Frequency Cepstral Coefficients, Power-Normalized Cepstral Coefficients, Perceptual Linear Prediction Cepstral Coefficients, Rasta-Perceptual Linear Prediction Cepstral Coefficients, Residue Cepstrum Coefficients and Linear Predictive Coding-derived cepstral coefficients extracted from codec compressed speech. The codecs selected for this study are G.711, G.729, G.722.2, Enhanced Voice Services, Mixed Excitation Linear Prediction and also three codecs based on compressive sensing frame work. The analysis also includes the variation in the quality of extracted features with various bit-rates supported by Enhanced Voice Services, G.722.2 and compressive sensing codecs. The quality analysis of extracted epochs, fundamental frequency and formants estimated from codec compressed speech was also performed here. In the case of various features extracted from the output of selected codecs, the variation introduced by Mixed Excitation Linear Prediction codec is the least due to its unique method for the representation of excitation. In the case of compressive sensing based codecs, there is a drastic improvement in the quality of extracted features with the augmentation of bit rate due to the waveform type coding used in compressive sensing based codecs. For the most popular Code Excited Linear Prediction codec based on Analysis-by-Synthesis coding paradigm, the impact of Linear Predictive Coding order in feature extraction is investigated. There is an improvement in the quality of extracted features with the order of linear prediction and the optimum performance is obtained for Linear Predictive Coding order between 20 and 30, and this varies with gender and statistical characteristics of speech. Even though the basic motive of a codec is to compress single voice source, the performance of codecs in multi speaker environment is also studied, which is the most common environment in majority of the speech processing applications. Here, the multi speaker environment with two speakers is considered and there is an augmentation in the quality of individual speeches with increase in diversity of mixtures that are passed through codecs. The perceptual quality of individual speeches extracted from the codec compressed speech is almost same for both Mixed Excitation Linear Prediction and Enhanced Voice Services codecs but regarding the preservation of features, the Mixed Excitation Linear Prediction codec has shown a superior performance over Enhanced Voice Services codec. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel multicomponent amplitude and frequency modulated (AFM) signal model for parametric representation of speech phonemes. An efficient technique is developed for parameter estimation of the proposed model. The Fourier-Bessel series expansion is used to separate a multicomponent speech signal into a set of individual components. The discrete energy separation algorithm is used to extract the amplitude envelope (AE) and the instantaneous frequency (IF) of each component of the speech signal. Then, the parameter estimation of the proposed AFM signal model is carried out by analysing the AE and IF parts of the signal component. The developed model is found to be suitable for representation of an entire speech phoneme (voiced or unvoiced) irrespective of its time duration, and the model is shown to be applicable for low bit-rate speech coding. The symmetric Itakura-Saito and the root-mean-square log-spectral distance measures are used for comparison of the original and reconstructed speech signals. [ABSTRACT FROM AUTHOR]

Practically, no knowledge exists on the effects of speech coding and recognition for narrow-band transmission of speech signals within certain frequency ranges especially in relation to the recognition of paralinguistic cues in speech. We thus investigated the impact of narrow-band standard speech coders on the machine-based classification of affective vocalizations and clinical vocal recordings. In addition, we analyzed the effect of speech low-pass filtering by a set of different cut-off frequencies, either chosen as static values in the 0.5-5-kHz range or given dynamically by different upper limits from the first five speech formants (F1-F5). Speech coding and recognition were tested, first, according to short-term speaker states by using affective vocalizations as given by the Geneva Multimodal Emotion Portrayals. Second, in relation to long-term speaker traits, we tested vocal recording from clinical populations involving speech impairments as found in the Child Pathological Speech Database. We employ a large acoustic feature space derived from the Interspeech Computational Paralinguistics Challenge. Besides analysis of the sheer corruption outcome, we analyzed the potential of matched and multicondition training as opposed to miss-matched condition. In the results, first, multicondition and matched-condition training significantly increase performances as opposed to mismatched condition. Second, downgrades in classification accuracy occur, however, only at comparably severe levels of low-pass filtering. The downgrades especially appear for multi-categorical rather than for binary decisions. These can be dealt with reasonably by the alluded strategies.

Multi-channel speech coding and enhancement is an indispensable technology in speech communication. In order to verify the effectiveness of multi-channel speech coding and enhancement methods in the research and development, a microphone array speech simulator (MASS) used in room acoustic environment is proposed. The proposed MASS is the improvement and extension of the existing multi-channel speech simulator. It aims to simulate clean speech, noisy speech, clean speech with reverberation, noisy speech with reverberation, and noise signals by microphone array used for multi-channel coding and enhancement of speech signal in room acoustic environment. The experimental results of the multi-channel speech coding and enhancement prove that the MASS could well simulate the signals used in real room acoustic environment and can be applied to the research of the related fields.

This article investigates switched quantizers for speech signal depicted with Gaussian probability density function (PDF). Gaussian PDF is better for smaller frame lengths that are represented here. Companding technique results in constant Signal to Quantization Noise Ratio (SQNR). In this paper two approaches are present: quasi-logarithmic (QL) and piecewise uniform (PU) compandor. Simpler compandor directly affects the complexity of hardware realization and expense of given solution, but, on the other hand, also brings to weaker performances. Therefore, a smart choice has to be made. Usage of switched technique leads to better performances. This way, the quality of quantization is improved by dividing the dynamic range of variances into multiple subranges. For each subrange a separate quantizer is designed, with its support region's amplitude. The optimal amplitude is numerically determined, whereby a single criterion is obtaining the maximal SQNR. Bit rates of these quantizers don't depend on signal variance, as the fixed length codes are used. The performances of proposed quantizers are demonstrated on real speech signals from the reliable database. Comparison of obtained results with other recent solutions is done in order to show the efficiency of this model. [ABSTRACT FROM AUTHOR]

This paper compares the effect of send-side music and environmental noise as background noise in a telephone communication. The study focuses on the quality experienced by the end user in the context of NB, WB and SWB mobile speech communication. The subjective test procedure defined in ITU-T Rec. P.835 is followed in this study. The results show that music as background noise in telephone conversation deteriorates the overall quality experienced by the end user. Moreover, the impact of music background noise on the quality is similar to that of the environmental noise. Furthermore it is shown that the music background noise seems to be slightly less intrusive than the environmental noise, especially when it comes to the lower SNR. [ABSTRACT FROM AUTHOR]

Speech coding is a field in which compression paradigms have not changed in the last 30 years. Speech signals are most commonly encoded with compression methods that have roots in linear predictive theory dating back to the early 1940s. This article bridges this influential theory with recent cognitive studies applicable in speech communication engineering. It reviews the mechanisms of speech perception that have led to perceptual speech coding. The focus is on human speech communication and machine learning and the application of cognitive speech processing in speech compression that presents a paradigm shift from perceptual (auditory) speech processing toward cognitive (auditory plus cortical) speech processing. [ABSTRACT FROM PUBLISHER]

This study introduces a new method for speech signal encryption and compression in a single step. The combined compression/encryption procedures are accomplished using compressive sensing (CS). The contourlet transform is used to increase the sparsity of the signal required by CS. Due to its randomness properties and very high sensitivity to initial conditions, the chaotic system is used to generate the sensing matrix of CS. This largely increases the key size of encryption to 10135 when logistic map is used. A spectral segmental signal‐to‐noise ratio of −36.813 dB is obtained as a measure of encryption strength. The quality of reconstructed speech is given by means of signal‐to‐noise ratio (SNR), and perceptual evaluation speech quality (PESQ). For 60% compression ratio the proposed method gives 48.203 dB SNR and 4.437 PESQ for voiced speech segments. However, for continuous speech (voiced and unvoiced), it gives 41.097 dB SNR and 4.321 PESQ. [ABSTRACT FROM AUTHOR]

In this paper a new speech privacy protection method based on sound masking and speech corpus is proposed. Frames in the speech corpus are selected according to secret keys and pitch of the destination speech frame, and the selected masking speech frames should have the same pitch period with the destination speech frame. Selected frames are used to mask the original speech frames and protect privacy of speech. Masked speech can be transmitted safely in communication system. By removing the effect of corresponding masking frames from the masked speech with the same corpus, original speech can be recovered. Experiment results show the proposed method gains good privacy protection effect and recovered speech quality. It is robust to speech compression by waveform coding algorithms, and helpful to systems using parametric coding algorithms for compression. [ABSTRACT FROM AUTHOR]

Steganography is a means of covert communication without revealing the occurrence and the real purpose of communication. The adaptive multirate wideband (AMR-WB) is a widely adapted format in mobile handsets and is also the recommended speech codec for VoLTE. In this paper, a novel AMR-WB speech steganography is proposed based on diameter-neighbor codebook partition algorithm. Different embedding capacity may be achieved by adjusting the iterative parameters during codebook division. The experimental results prove that the presented AMR-WB steganography may provide higher and flexible embedding capacity without inducing perceptible distortion compared with the state-of-the-art methods. With 48 iterations of cluster merging, twice the embedding capacity of complementary-neighbor-vertices-based embedding method may be obtained with a decrease of only around 2% in speech quality and much the same undetectability. Moreover, both the quality of stego speech and the security regarding statistical steganalysis are better than the recent speech steganography based on neighbor-index-division codebook partition. [ABSTRACT FROM AUTHOR]

In this paper a delta modulation speech coding scheme based on the ITU-T G.711 standard and the switched first-order predictor is presented. The forward adaptive scheme is used, where the adaptation to the signal variance is performed on frame-by-frame basis. The classification of the frames into weakly and highly correlated was done based on the correlation coefficient calculated for each frame, providing a basis for choosing the appropriate predictor coefficient. The obtained results indicate that the proposed model significantly outperforms the scalar companding system based on the G.711 standard. The obtained experimental results were verified using the theoretical model in the wide dynamic range of the input variance. [ABSTRACT FROM AUTHOR]

Speaker recognition for different languages is still a big challenge for researchers. The accuracy of identification rate (IR) is great issue, if the utterance of speech sample is less. This paper aims to implement speaker recognition for Hindi speech samples using Mel frequency cepestral coffiecient–vector quantization (MFCC-VQ) and Mel frequency cepestral cofficient-Gaussian mixture model (MFCC-GMM) for text dependent and text independent phrases. The accuracy of text independent recognition by MFCC-VQ and MFCC-GMM for Hindi speech sample is 77.64% and 86.27% respectively. However, the accuracy has increased significantly for text dependent recognition. The accuracy of Hindi speech samples are 85.49 % and 94.12 % using MFCC-VQ and MFCC-GMM approach. We have tested 15 speakers consisting 10 male and 5 female speakers. The total number of trails for each speaker is 17. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of learning a classifier for the classification of spoken character. We present a solution based on Group Method of Data Handling (GMDH) learning paradigm for the development of a robust abductive network classifier. We improve the reliability of the classification process by introducing the concept of multiple abductive network classifier system. We evaluate the performance of the proposed classifier using three different speech datasets including spoken Arabic digit, spoken English letter, and spoken Pashto digit. The performance of the proposed classifier surpasses that reported in the literature for other classification techniques on the same speech datasets. [ABSTRACT FROM AUTHOR]

Digital watermarking technology is concerned with solving the problem of copyright protection, data authentication, content identification, distribution, and duplication of the digital media due to the great developments in computers and Internet technology. Recently, protection of digital audio signals has attracted the attention of researchers. This paper proposes a new audio watermarking scheme based on discrete wavelet transform (DWT), singular value decomposition (SVD), and quantization index modulation (QIM) with a synchronization code embedded with two encrypted watermark images or logos inserted into a stereo audio signal. In this algorithm, the original audio signal is split into blocks, and each block is decomposed with a two-level DWT, and then the approximate low-frequency sub-band coefficients are decomposed by SVD transform to obtain a diagonal matrix. The prepared watermarking and synchronization code bit stream is embedded into the diagonal matrix using QIM. After that, we perform inverse singular value decomposition (ISVD) and inverse discrete wavelet transform (IDWT) to obtain the watermarked audio signal. The watermark can be blindly extracted without knowledge of the original audio signal. Experimental results show that the transparency and imperceptibility of the proposed algorithm is satisfied, and that robustness is strong against popular audio signal processing attacks. High watermarking payload is achieved through the proposed scheme. [ABSTRACT FROM AUTHOR]

State-of-the-art speech recognition solutions currently employ hidden Markov models (HMMs) to capture the time variability in a speech signal and deep neural networks (DNNs) to model the HMM state distributions. It has been shown that DNN-HMM hybrid systems outperform traditional HMM and Gaussian mixture model (GMM) hybrids in many applications. This improvement is mainly attributed to the ability of DNNs to model more complex data structures. However, having sufficient data samples is one key point in training a high accuracy DNN as a discriminative model. This barrier makes DNNs unsuitable for many applications with limited amounts of data. In this study, we introduce a method to produce an excessive amount of pseudo-samples that requires availability of only a small amount of transcribed data from the target domain. In this method, a universal background model (UBM) is trained to capture a parametric estimate of the data distributions. Next, random sampling is used to generate a large amount of pseudo-samples from the UBM. Frame-Shuffling is then applied to smooth the temporal cepstral trajectories in the generated pseudo-sample sequences to better resemble the temporal characteristics of a natural speech signal. Finally, the pseudo-sample sequences are combined with the original training data to train the DNN-HMM acoustic model of a speech recognizer. The proposed method is evaluated on small-sized sets of neutral and whisper data drawn from the UT-Vocal Effort II corpus. It is shown that phoneme error rates (PERs) of a DNN-HMM based speech recognizer are considerably reduced when incorporating the generated pseudo-samples in the training process, with + 79.0 and + 45.6% relative PER improvements for neutral-neutral training/test and whisper-whisper training/test scenarios, respectively. [ABSTRACT FROM AUTHOR]

This work explores the characteristics of speech in terms of the spectral characteristics of vocal tract system for deriving features effective for clean speech and speech with background music classification. A representation of the spectral characteristics of the vocal tract system in the form of Hilbert envelope of the numerator of group delay (HNGD) spectrum is explored for the task. This representation complements the existing methods of computing the spectral characteristics in terms of the temporal resolution. This spectrum has an additive and high resolution property which gives a better representation of the formants especially the higher ones. A feature is extracted from the HNGD spectrum which is known as the spectral contrast across the sub-bands and this feature essentially represents the relative spectral characteristics of the vocal tract system. The vocal tract system is also represented approximately in terms of the mel frequency cepstral coefficients (MFCCs) which represent the average spectral characteristics. The MFCCs and the sum of the spectral contrast on HNGD can be used as features to represent the average and relative spectral characteristics of the vocal tract system, respectively. These features complement each other and can be combined in a multidimensional framework to provide good discrimination between clean speech and speech with background music segments. The spectral contrast on HNGD spectrum is compared to the spectral contrast on discrete fourier transform (DFT) spectrum, which also represents the relative spectral characteristics of the vocal tract system. It is observed that better performances are achieved on the HNGD spectrum than the DFT spectrum. The features are classified using classifiers like Gaussian mixture models and support vector machines. [ABSTRACT FROM AUTHOR]

Designing text-to-speech systems capable of producing natural sounding speech segments in different Indian languages is a challenging and ongoing problem. Due to the large number of possible pronunciations in different Indian languages, a number of speech segments are needed to be stored in the speech database while a concatenative speech synthesis technique is used to achieve highly natural speech segments. However, the large speech database size makes it unusable for small hand held devices or human computer interactive systems with limited storage resources. In this paper, we proposed a fraction-based waveform concatenation technique to produce intelligible speech segments from a small footprint speech database. The results of all the experiments performed shows the effectiveness of the proposed technique in producing intelligible speech segments in different Indian languages even with very less storage and computation overhead compared to the existing syllable-based technique. [ABSTRACT FROM AUTHOR]

With the continuous development of the internet technology, its application in the machine translation field is becoming more and more popular. Combining the information technology with the machine translation technology, the internet-based machine translation system accesses the English and Chinese bilingual parallel information via the internet and completes translation with artificial assistance, which breaks the reliance on the machine. In order to verify the system, this paper studies the internet based English machine translation system and finds that it can complete rapid and effective translation with higher quality and efficiency compared with traditional machine translation methods. Therefore, the system is worth being promoted and has a good development prospect. [ABSTRACT FROM AUTHOR]

The interesting aspect of the Dravidian languages is a commonality through a shared script, similar vocabulary, and their common root language. In this work, an attempt has been made to classify the four complex Dravidian languages using cepstral coefficients and prosodic features. The speech of Dravidian languages has been recorded in various environments and considered as a database. It is demonstrated that while cepstral coefficients can indeed identify the language correctly with a fair degree of accuracy, prosodic features are added to the cepstral coefficients to improve language identification performance. Legendre polynomial fitting and the principle component analysis (PCA) are applied on feature vectors to reduce dimensionality which further resolves the issue of time complexity. In the experiments conducted, it is found that using both cepstral coefficients and prosodic features, a language identification rate of around 87% is obtained, which is about 18% above the baseline system using Mel-frequency cepstral coefficients (MFCCs). It is observed from the results that the temporal variations and prosody are the important factors needed to be considered for the tasks of language identification. [ABSTRACT FROM AUTHOR]

Nowadays, according to ever-increasing volumes of audio content, audio processing is a vital need. In the aerospace field, voice commands could be used instead of data commands in order to speed up the command transmission, help crewmembers to complete their tasks by allowing hands-free control of supplemental equipment and as a redundant system for increasing the reliability of command transmission. In this paper, a voice command detection (VCD) framework is proposed for aerospace applications, which decodes the voice commands to comprehensible and executable commands, in an acceptable speed with a low false alarm rate. The framework is mainly based on a keyword spotting method, which extracts some pre-defined target keywords from the input voice commands. The mentioned keywords are input arguments to the proposed rule-based language model (LM). The rule-based LM decodes the voice commands based on the input keywords and their locations. Two keyword spotters are trained and used in the VCD system. The phone-based keyword spotter is trained on TIMIT database. Then, speaker adaptation methods are exploited to modify the parameters of the trained models using non-native speaker utterances. The word-based keyword spotter is trained on a database prepared and specialized for aerospace applications. The experimental results show that the word-based VCD system decodes the voice commands with true detection rate equal to 88% and false alarm rate equal to 12%, in average. Additionally, using speaker adaptation methods in the phone-based VCD system improves the true detection and false alarm rates about 21% and 21%, respectively. [ABSTRACT FROM AUTHOR]

Multi-channel blind source separation (BSS) methods use more than one microphone. There is a need to develop speech separation algorithms under single microphone scenario. In this paper we propose a method for single channel speech separation (SCSS) by combining empirical mode decomposition (EMD) and speech specific information. Speech specific information is derived in the form of source-filter features. Source features are obtained using multi pitch information. Filter information is estimated using formant analysis. To track multi pitch information in the mixed signal we apply simple-inverse filtering tracking (SIFT) and histogram based pitch estimation to excitation source information. Formant estimation is done using linear predictive (LP) analysis. Pitch and formant estimation are done with and without EMD decomposition for better extraction of the individual speakers in the mixture. Combining EMD with speech specific information provides encouraging results for single-channel speech separation. [ABSTRACT FROM AUTHOR]