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In our previous work, we introduced our Bayesian Hidden Markov Model with eigenvoice priors, which has been recently recognized as the state-of-the-art model for Speaker Diarization. In this article we present a more complete analysis of the Diarization system. The inference of the model is fully described and derivations of all update formulas are provided for a complete understanding of the algorithm. An extensive analysis on the effect, sensitivity and interactions of all model parameters is provided, which might be used as a guide for their optimal setting. The newly introduced speaker regularization coefficient allows us to control the number of speakers inferred in an utterance. A naive speaker model merging strategy is also presented, which allows to drive the variational inference out of local optima. Experiments for the different diarization scenarios are presented on CALLHOME and DIHARD datasets.

In recent years, a number of time-domain speech separation methods have been proposed. However, most of them are very sensitive to the environments and wide domain coverage tasks. In this paper, from the time-frequency domain perspective, we propose a densely-connected pyramid complex convolutional network, termed DPCCN, to improve the robustness of speech separation under complicated conditions. Furthermore, we generalize the DPCCN to target speech extraction (TSE) by integrating a new specially designed speaker encoder. Moreover, we also investigate the robustness of DPCCN to unsupervised cross-domain TSE tasks. A Mixture-Remix approach is proposed to adapt the target domain acoustic characteristics for fine-tuning the source model. We evaluate the proposed methods not only under noisy and reverberant in-domain condition, but also in clean but cross-domain conditions. Results show that for both speech separation and extraction, the DPCCN-based systems achieve significantly better performance and robustness than the currently dominating time-domain methods, especially for the cross-domain tasks. Particularly, we find that the Mixture-Remix fine-tuning with DPCCN significantly outperforms the TD-SpeakerBeam for unsupervised cross-domain TSE, with around 3.5 dB SISNR improvement on target domain test set, without any source domain performance degradation., accepted by ICASSP 2022

The paper presents a study of usability of x-vectors for adaptation of automatic speech recognition (ASR) systems. X-vectors are Neural Network (NN)-based speaker embeddings recently proposed in speaker recognition (SR). They quickly replaced common i-vectors and became new state-of-the-art technique. Here, the same approach is adopted for ASR with the hope of similar outcome. All experiments were done on ASR for the latest IARPA MATERIAL evaluation running on Pashto language. Over 1% absolute improvement was observed with x-vectors over traditional i-vectors, even when the x-vector extractor was not trained on target Pashto data.

The processing of speech corrupted by interfering overlapping speakers is one of the challenging problems with regards to today's automatic speech recognition systems. Recently, approaches based on deep learning have made great progress toward solving this problem. Most of these approaches tackle the problem as speech separation, i.e., they blindly recover all the speakers from the mixture. In some scenarios, such as smart personal devices, we may however be interested in recovering one target speaker from a mixture. In this paper, we introduce SpeakerBeam, a method for extracting a target speaker from the mixture based on an adaptation utterance spoken by the target speaker. Formulating the problem as speaker extraction avoids certain issues such as label permutation and the need to determine the number of speakers in the mixture. With SpeakerBeam, we jointly learn to extract a representation from the adaptation utterance characterizing the target speaker and to use this representation to extract the speaker. We explore several ways to do this, mostly inspired by speaker adaptation in acoustic models for automatic speech recognition. We evaluate the performance on the widely used WSJ0-2mix and WSJ0-3mix datasets, and these datasets modified with more noise or more realistic overlapping patterns. We further analyze the learned behavior by exploring the speaker representations and assessing the effect of the length of the adaptation data. The results show the benefit of including speaker information in the processing and the effectiveness of the proposed method.

Self-supervised ASR-TTS models suffer in out-of-domain data conditions. Here we propose an enhanced ASR-TTS (EAT) model that incorporates two main features: 1) The ASR$\rightarrow$TTS direction is equipped with a language model reward to penalize the ASR hypotheses before forwarding it to TTS. 2) In the TTS$\rightarrow$ASR direction, a hyper-parameter is introduced to scale the attention context from synthesized speech before sending it to ASR to handle out-of-domain data. Training strategies and the effectiveness of the EAT model are explored under out-of-domain data conditions. The results show that EAT reduces the performance gap between supervised and self-supervised training significantly by absolute 2.6\% and 2.7\% on Librispeech and BABEL respectively.

We launched a community platform for collecting the ATC speech world-wide in the ATCO2 project. Filtering out unseen non-English speech is one of the main components in the data processing pipeline. The proposed English Language Detection (ELD) system is based on the embeddings from Bayesian subspace multinomial model. It is trained on the word confusion network from an ASR system. It is robust, easy to train, and light weighted. We achieved 0.0439 equal-error-rate (EER), a 50% relative reduction as compared to the state-of-the-art acoustic ELD system based on x-vectors, in the in-domain scenario. Further, we achieved an EER of 0.1352, a 33% relative reduction as compared to the acoustic ELD, in the unseen language (out-of-domain) condition. We plan to publish the evaluation dataset from the ATCO2 project.

In this paper, we propose a method combining variational autoencoder model of speech with a spatial clustering approach for multi-channel speech separation. The advantage of integrating spatial clustering with a spectral model was shown in several works. As the spectral model, previous works used either factorial generative models of the mixed speech or discriminative neural networks. In our work, we combine the strengths of both approaches, by building a factorial model based on a generative neural network, a variational autoencoder. By doing so, we can exploit the modeling power of neural networks, but at the same time, keep a structured model. Such a model can be advantageous when adapting to new noise conditions as only the noise part of the model needs to be modified. We show experimentally, that our model significantly outperforms previous factorial model based on Gaussian mixture model (DOLPHIN), performs comparably to integration of permutation invariant training with spatial clustering, and enables us to easily adapt to new noise conditions. The code for the method is available at https://github.com/BUTSpeechFIT/vae_dolphin, Comment: 8 pages, 3 figures, to be published in SLT2021

SpecAugment is a newly proposed data augmentation method for speech recognition. By randomly masking bands in the log Mel spectogram this method leads to impressive performance improvements. In this paper, we investigate the usage of SpecAugment for speaker verification tasks. Two different models, namely 1-D convolutional TDNN and 2-D convolutional ResNet34, trained with either Softmax or AAM-Softmax loss, are used to analyze SpecAugment’s effectiveness. Experiments are carried out on the Voxceleb and NIST SRE 2016 dataset. By applying SpecAugment to the original clean data in an on-the-fly manner without complex off-line data augmentation methods, we obtained 3.72% and 11.49% EER for NIST SRE 2016 Cantonese and Tagalog, respectively. For Voxceleb1 evaluation set, we obtained 1.47% EER.

In this work, we propose a hierarchical subspace model for acoustic unit discovery. In this approach, we frame the task as one of learning embeddings on a low-dimensional phonetic subspace, and simultaneously specify the subspace itself as an embedding on a hyper-subspace. We train the hyper-subspace on a set of transcribed languages and transfer it to the target language. In the target language, we infer both the language and unit embeddings in an unsupervised manner, and in so doing, we simultaneously learn a subspace of units specific to that language and the units that dwell on it. We conduct our experiments on TIMIT and two low-resource languages: Mboshi and Yoruba. Results show that our model outperforms major acoustic unit discovery techniques, both in terms of clustering quality and segmentation accuracy., Comment: Submitted to ICASSP 2021

Multichannel speech processing applications usually employ beamformers as means of speech enhancement through spatial filtering. Beamformers with learnable parameters require training to minimize a loss function that is not necessarily correlated with the final objective. In this paper, we present a framework employing recent neural network based generalized eigenvalue beamformer and application-specific model that allows for optimization of beamformer w.r.t. target application. In our case, the application is speaker verification which utilizes a speaker embedding (x-vector) extractor that conveniently comes with desired loss. We show that application-specific training of the beamformer brings performance improvements over a system trained in the standard way. We perform our analysis on the recently introduced VOiCES corpus which contains multichannel data and allows us to modify the evaluation trials such that enrollment recordings remain single-channel and test utterances are multichannel.

DeepMine is a speech database in Persian and English designed to build and evaluate text-dependent, text-prompted, and text-independent speaker verification, as well as Persian speech recognition systems. It contains more than 1850 speakers and 540 thousand recordings overall, more than 480 hours of speech are transcribed. It is the first public large-scale speaker verification database in Persian, the largest public text-dependent and text-prompted speaker verification database in English, and the largest public evaluation dataset for text-independent speaker verification. It has a good coverage of age, gender, and accents. We provide several evaluation protocols for each part of the database to allow for research on different aspects of speaker verification. We also provide the results of several experiments that can be considered as baselines: HMM-based i-vectors for text-dependent speaker verification, and HMM-based as well as state-of-the-art deep neural network based ASR. We demonstrate that the database can serve for training robust ASR models.

This paper presents BUT ReverbDB - a dataset of real room impulse responses (RIR), background noises and re-transmitted speech data. The retransmitted data includes LibriSpeech test-clean, 2000 HUB5 English evaluation and part of 2010 NIST Speaker Recognition Evaluation datasets. We provide a detailed description of RIR collection (hardware, software, post-processing) that can serve as a "cook-book" for similar efforts. We also validate BUT ReverbDB in two sets of automatic speech recognition (ASR) experiments and draw conclusions for augmenting ASR training data with real and artificially generated RIRs. We show that a limited number of real RIRs, carefully selected to match the target environment, provide results comparable to a large number of artificially generated RIRs, and that both sets can be combined to achieve the best ASR results. The dataset is distributed for free under a non-restrictive license and it currently contains data from 8 rooms, which is growing. The distribution package also contains a Kaldi-based recipe for augmenting publicly available AMI close-talk meeting data and test the results on an AMI single distant microphone set, allowing it to reproduce our experiments., Submitted to Journal of Selected Topics in Signal Processing, November 2018

This paper addresses the problem of recognizing speech corrupted by overlapping speakers in a multichannel setting. To extract a target speaker from the mixture, we use a neural network based beamformer which uses masks estimated by a neural network to compute statistically optimal spatial filters. Following our previous work, we inform the neural network about the target speaker using information extracted from an adaptation utterance’ enabling the network to track the target speaker. While in the previous work, this method was used to separately extract the speaker and then pass such preprocessed speech to a speech recognition system, here we explore training both systems jointly with a common speech recognition criterion. We show that integrating the two systems and training for the final objective improves the performance. In addition, the integration enables further sharing of information between the acoustic model and the speaker extraction system, by making use of the predicted HMM-state posteriors to refine the masks used for beamforming.

This paper deals with far-field speaker recognition. On a corpus of NIST SRE 2010 data retransmitted in a real room with multiple microphones, we first demonstrate how room acoustics cause significant degradation of state-of-the-art i-vector based speaker recognition system. We then investigate several techniques to improve the performances ranging from probabilistic linear discriminant analysis (PLDA) re-training, through dereverberation, to beamforming. We found that weighted prediction error (WPE) based dereverberation combined with generalized eigenvalue beamformer with power-spectral density (PSD) weighting masks generated by neural networks (NN) provides results approaching the clean close-microphone setup. Further improvement was obtained by re-training PLDA or the mask-generating NNs on simulated target data. The work shows that a speaker recognition system working robustly in the far-field scenario can be developed.

The paper provides an analysis of automatic speech recognition systems (ASR) based on multilingual BLSTM, where we used multi-task training with separate classification layer for each language. The focus is on low resource languages, where only a limited amount of transcribed speech is available. In such scenario, we found it essential to train the ASR systems in a multilingual fashion and we report superior results obtained with pre-trained multilingual BLSTM on this task. The high resource languages are also taken into account and we show the importance of language richness for multilingual training. Next, we present the performance of this technique as a function of amount of target language data. The importance of including context information into BLSTM multilingual systems is also stressed, and we report increased resilience of large NNs to overtraining in case of multi-task training.

In this paper, we present promising accurate prefix boosting (PAPB), a discriminative training technique for attention based sequence-to-sequence (seq2seq) ASR. PAPB is devised to unify the training and testing scheme in an effective manner. The training procedure involves maximizing the score of each partial correct sequence obtained during beam search compared to other hypotheses. The training objective also includes minimization of token (character) error rate. PAPB shows its efficacy by achieving 10.8\% and 3.8\% WER with and without RNNLM respectively on Wall Street Journal dataset.

Recently, speaker embeddings extracted with deep neural networks became the state-of-the-art method for speaker verification. In this paper we aim to facilitate its implementation on a more generic toolkit than Kaldi, which we anticipate to enable further improvements on the method. We examine several tricks in training, such as the effects of normalizing input features and pooled statistics, different methods for preventing overfitting as well as alternative non-linearities that can be used instead of Rectifier Linear Units. In addition, we investigate the difference in performance between TDNN and CNN, and between two types of attention mechanism. Experimental results on Speaker in the Wild, SRE 2016 and SRE 2018 datasets demonstrate the effectiveness of the proposed implementation.

Recent work on Acoustic Unit Discovery (AUD) has led to the development of a non-parametric Bayesian phone-loop model where the prior over the probability of the phone-like units is assumed to be sampled from a Dirichlet Process (DP). In this work, we propose to improve this model by incorporating a Hierarchical Pitman-Yor based bigram Language Model on top of the units' transitions. This new model makes use of the phonotactic context information but assumes a fixed number of units. To remedy this limitation we first train a DP phone-loop model to infer the number of units, then, the bigram phone-loop is initialized from the DP phone-loop and trained until convergence of its parameters. Results show an absolute improvement of 1–2%on the Normalized Mutual Information (NMI) metric. Furthermore, we show that, combined with Multilingual Bottleneck (MBN) features the model yields a same or higher NMI as an English phone recogniser trained on TIMIT.

This paper provides an extensive summary of BUT 2016 system for the last IARPA Babel evaluations. It concentrates on multi-lingual training of both deep neural network (DNN)-based feature extraction and acoustic models including multilingual training of bidirectional Long Short Term memory networks. Next, two low-dimensional vector approaches to speaker adaptation are investigated: i-vectors and sequence-summarizing neural networks (SSNN). The results provided on three Babel Year 4 languages show clear advantage of both approaches in case limited amount of training data is available. The time necessary for the development of a new system is addressed too, as some of the investigated techniques do not require extensive re-training of the whole system.

This paper analyzes the behavior of our state-of-the-art Deep Neural Network/i-vector/PLDA-based speaker recognition systems in multi-language conditions. On the “Language Pack” of the PRISM set, we evaluate the systems' performance using the NIST's standard metrics. We show that not only the gain from using DNNs vanishes, nor using dedicated DNNs for target conditions helps, but also the DNN-based systems tend to produce de-calibrated scores under the studied conditions. This work gives suggestions for directions of future research rather than any particular solutions to these issues.

This work studies the usage of the Deep Neural Network (DNN) Bottleneck (BN) features together with the traditional MFCC features in the task of i-vector-based speaker recognition. We decouple the sufficient statistics extraction by using separate GMM models for frame alignment, and for statistics normalization and we analyze the usage of BN and MFCC features (and their concatenation) in the two stages. We also show the effect of using full-covariance GMM models, and, as a contrast, we compare the result to the recent DNN-alignment approach. On the NIST SRE2010, telephone condition, we show 60% relative gain over the traditional MFCC baseline for EER (and similar for the NIST DCF metrics), resulting in 0.94% EER.

In recent years, trained feature extraction (FE) schemes based on neural networks have replaced or complemented traditional approaches in top performing systems. This paper deals with FE in multilingual scenarios with a target language with low amount of transcribed data. Continuing our previous work on multilingual training of Stacked Bottle-Neck Neural Network FE schemes, we concentrate on improving the discriminatively trained Region-Dependent Transforms. We show that multilingual training of RDT can be implemented by merging statistics from several languages. In our case we used up to 11 source languages to build a FE which generalize well for a new language. This allows us to build a strong bootstrapping model for the final ASR system. The results are produced on IARPA Babel data.

In this paper, we describe our work on the ASpIRE (Automatic Speech recognition In Reverberant Environments) challenge, which aims to assess the robustness of automatic speech recognition (ASR) systems. The main characteristic of the challenge is developing a high-performance system without access to matched training and development data. While the evaluation data are recorded with far-field microphones in noisy and reverberant rooms, the training data are telephone speech and close talking. Our approach to this challenge includes speech enhancement, neural network methods and acoustic model adaptation, We show that these techniques can successfully alleviate the performance degradation due to noisy audio and data mismatch.

In this paper, several feature extraction and channel compensation techniques found in state-of-the-art speaker verification systems are analyzed and discussed. For the NIST SRE 2006 submission, cepstral mean subtraction, feature warping, RelAtive SpecTrAl (RASTA) filtering, heteroscedastic linear discriminant analysis (HLDA), feature mapping, and eigenchannel adaptation were incrementally added to minimize the system's error rate. This paper deals with eigenchannel adaptation in more detail and includes its theoretical background and implementation issues. The key part of the paper is, however, the post-evaluation analysis, undermining a common myth that ldquothe more boxes in the scheme, the better the system.rdquo All results are presented on NIST Speaker Recognition Evaluation (SRE) 2005 and 2006 data.

This paper describes and discusses the "STBU" speaker recognition system, which performed well in the NIST Speaker Recognition Evaluation 2006 (SRE). STBU is a consortium of four partners: Spescom DataVoice (Stellenbosch, South Africa), TNO (Soesterberg, The Netherlands), BUT (Brno, Czech Republic), and the University of Stellenbosch (Stellenbosch, South Africa). The STBU system was a combination of three main kinds of subsystems: 1) GMM, with short-time Mel frequency cepstral coefficient (MFCC) or perceptual linear prediction (PLP) features, 2) Gaussian mixture model-support vector machine (GMM-SVM), using GMM mean supervectors as input to an SVM, and 3) maximum-likelihood linear regression-support vector machine (MLLR-SVM), using MLLR speaker adaptation coefficients derived from an English large vocabulary continuous speech recognition (LVCSR) system. All subsystems made use of supervector subspace channel compensation methods-either eigenchannel adaptation or nuisance attribute projection. We document the design and performance of all subsystems, as well as their fusion and calibration via logistic regression. Finally, we also present a cross-site fusion that was done with several additional systems from other NIST SRE-2006 participants. © 2006 IEEE.

The project Augmented Multi-party Interaction (AMI) is concerned with the development of meeting browsers and remote meeting assistants for instrumented meeting rooms – and the required component technologies R&D themes: group dynamics, audio, visual, and multimodal processing, content abstraction, and human-computer interaction. The audio-visual processing workpackage within AMI addresses the automatic recognition from audio, video, and combined audio-video streams, that have been recorded during meetings. In this article we describe the progress that has been made in the first two years of the project. We show how the large problem of audio-visual processing in meetings can be split into seven questions, like “Who is acting during the meeting?��?. We then show which algorithms and methods have been developed and evaluated for the automatic answering of these questions.

The paper investigates into Query by Example (QbE) - a spoken term detection technique with queries entered by voice. It describes BUT QbE system that achieved the best accuracy in MediaEval QUESST2014 evaluations. This evaluation was challenging because of severe mismatch between queries and utterances, and introduction of new types of queries. The paper provides an analysis of DTW sub-system's in mismatched conditions (especially targeting DTW metrics) and discusses approaches investigated for QUESST2014: generation of calibration side-information by a language identification system, and handling T2 and T3 queries relaxing the constraints of an exact match. All results are provided on QUESST2014 development and evaluation data.

The paper describes Brno University of Technology (BUT) ASR system for 2014 BABEL Surprise language evaluation (Tamil). While being largely based on our previous work, two original contributions were brought: (1) speaker-adapted bottle-neck neural network (BN) features were investigated as an input to DNN recognizer and semi-supervised training was found effective. (2) Adding of noise to training data outperformed a classical de-noising technique while dealing with noisy test data was found beneficial, and the performance of this approach was verified on a relatively clean training/test data setup from a different language. All results are reported on BABEL 2014 Tamil data.

This paper presents our work on speech recognition of Vietnamese spontaneous telephone conversations. It focuses on feature extraction by Stacked Bottle-Neck neural networks: several improvements such as semi-supervised training on untranscribed data, increasing of precision of state targets, and CMLLR adaptations were investigated. We have also tested speaker adaptive training of this architecture and significant gain was found. The results are reported on BABEL Vietnamese data. Index Terms: speech recognition, discriminative training, bottleneck neural networks, adaptation of neural networks, regiondependent transforms

This paper summarizes our work for MediaEval 2013 Spoken Web Search task evaluations. The task was Query-by-Example (search of spoken queries within spoken data). We submitted a system composed of 26 subsystems, of which 13 arebased on Acoustic Keyword Spotting and 13 on Dynamic Time Warping. All of them use threestate phoneme posteriors as input features. Our main contribution was m-norm normalization of particular subsystems together with the fusion based on binary logistic regression. The results, including per-language analysis, are provided on MediaEval 2013 dataset.

The paper addresses manual and semi-automatic approaches to building a multilingual phoneme set for automatic speech recognition. The first approach involves mapping and reduction of the phoneme set based on IPA and expert knowledge, the later one involves phoneme confusion matrix generated by a neural network. The comparison is done for 8 languages selected from GlobalPhone on three scenarios: 1) multilingual system with abundant data for all the languages, 2) multilingual systems excluding target language 3) multilingual systems with small amount of data for target languages. For 3), the multilingual system brought improvement for languages close enough to the others in the set.

In today's speech recognition systems, linear or nonlinear transformations are usually applied to post-process speech features forming input to HMM based acoustic models. In this work, we experiment with three popular transforms: HLDA, MPE-HLDA and Region Dependent Linear Transforms (RDLT), which are trained jointly with the acoustic model to extract maximum of the discriminative information from the raw features and to represent it in a form suitable for the following GMM-HMM based acoustic model. We focus on multi-lingual environments, where limited resources are available for training recognizers of many languages. Using data from GlobalPhone database, we show that, under such restrictive conditions, the feature transformations can be advantageously shared across languages and robustly trained using data from several languages.

Phonotactic models based on bags of n-grams representations and discriminative classifiers are a popular approach to the language recognition problem. However, the large size of n-gram count vectors brings about some difficulties in discriminative classifiers. The subspace Multinomial model was recently proposed to effectively represent information contained in the n-grams using low-dimensional iVectors. The availability of a low-dimensional feature vector allows investigating different post-processing techniques and different classifiers to improve recognition performance. In this work, we analyze a set of discriminative classifiers based on Support Vector Machines and Logistic Regression and we propose an iVector post-processing technique which allows to improve recognition performance. The proposed systems are evaluated on the NIST LRE 2009 task.

We presented a novel technique for discriminative feature-level adaptation of automatic speech recognition system. The concept of iVectors popular in Speaker Recognition is used to extract information about speaker or acoustic environment from speech segment. iVector is a low-dimensional fixed-length representing such information. To utilized iVectors for adaptation, Region Dependent Linear Transforms (RDLT) are discriminatively trained using MPE criterion on large amount of annotated data to extract the relevant information from iVectors and to compensate speech feature. The approach was tested on standard CTS data. We found it to be complementary to common adaptation techniques. On a well tuned RDLT system with standard CMLLR adaptation we reached 0.8% additive absolute WER improvement.

We describe how to effectively train neural network based language models on large data sets. Fast convergence during training and better overall performance is observed when the training data are sorted by their relevance. We introduce hash-based implementation of a maximum entropy model, that can be trained as a part of the neural network model. This leads to significant reduction of computational complexity. We achieved around 10% relative reduction of word error rate on English Broadcast News speech recognition task, against large 4-gram model trained on 400M tokens.

We present several modifications of the original recurrent neural network language model (RNN LM).While this model has been shown to significantly outperform many competitive language modeling techniques in terms of accuracy, the remaining problem is the computational complexity. In this work, we show approaches that lead to more than 15 times speedup for both training and testing phases. Next, we show importance of using a backpropagation through time algorithm. An empirical comparison with feedforward networks is also provided. In the end, we discuss possibilities how to reduce the amount of parameters in the model. The resulting RNN model can thus be smaller, faster both during training and testing, and more accurate than the basic one.

We describe recent progress in the field of prosodic modeling for speaker verification. In a previous paper, we proposed a technique for modeling syllable-based prosodic features that uses a multinomial subspace model for feature extraction and within-class covariance normalization or linear discriminant analysis for session variability compensation. In this paper, we show that performance can be significantly improved with the use of probabilistic linear discriminant analysis (PLDA) for session variability compensation. This system does not require score normalization. We report an equal error rate below 7% on a NIST 2008 task. To our knowledge, this is the best reported result to date for a prosodic system for speaker recognition. Fusion of this system with a state-of-the-art acoustic baseline system yields 10% relative improvement in the new detection cost function (DCF) as defined by NIST.

In this paper, we describe recent progress in i-vector based speaker verification. The use of universal background models (UBM) with full-covariance matrices is suggested and thoroughly experimentally tested. The i-vectors are scored using a simple cosine distance and advanced techniques such as Probabilistic Linear Discriminant Analysis (PLDA) and heavy-tailed variant of PLDA (PLDA-HT). Finally, we investigate into dimensionality reduction of i-vectors before entering the PLDA-HT modeling. The results are very competitive: on NIST 2010 SRE task, the results of a single full-covariance LDA-PLDA-HT system approach those of complex fused system.

The paper deals with the development of acoustic keyword spotter (KWS) meeting requirements of a real user from the security community. While the basic scheme of the KWS is relatively standard, it uses novel features derived by a hierarchy of neural networks, and score normalization trained to maximize a user-like evaluation metric. The results are reported on a selection of Czech conversational telephone speech (CTS), radio and read data.

This paper describes an innovative web-based browser used for video recordings of lectures that is built on speech and image processing technologies. The aim of this project is to simplify the access to information that is spread across video recordings. This is mainly achieved by coupling to the speech search engine and due to a possibility to quickly navigate through an automatically generated list of slides presented. The reader is briefly acquainted with the technological background of the browser; the emphasis is laid on the use of the browser from the user point of view.

Phonotactic approach, phone recognition to be followed by language modeling, is one of the most popular approaches to language identification (LID). In this work, we explore how language identification accuracy of a phone decoder can be enhanced by varying acoustic resolution of the phone decoder, and subsequently how multiresolution versions of the same decoder can be integrated to improve the LID accuracy. We use mutual information to select the optimum set of phones for a specific acoustic resolution. Further, we propose strategies for building multilingual systems suitable for LID applications, and subsequently fine tune these systems to enhance the overall accuracy.

We investigate various ways of generating prosodic syllable contour features that have recently been applied to enhance systems for speaker recognition. We compare different approaches for segmentation of speech into syllable-like units, techniques for contour modeling and the extraction of pitch and energy, taking into account the computational complexity and gender dependence. We show that the performance is especially affected by the segmentation and the quality of the pitch tracking algorithm and that the features are highly gender dependent. Still, computationally simple ways of segmentation of speech can be used to achieve good results, as experiments on 2006 NIST speaker recognition evaluation task indicate.

Speech recognition of inflectional and morphologically rich languages like Czech is currently quite a challenging task, because simple n-gram techniques are unable to capture important regularities in the data. Several possible solutions were proposed, namely class based models, factored models, decision trees and neural networks. This paper describes improvements obtained in recognition of spoken Czech lectures using language models based on neural networks. Relative reductions in word error rate are more than 15% over baseline obtained with adapted 4-gram backoff language model using modified Kneser-Ney smoothing.

In this paper, we are concerned with using decision trees (DT) and random forests (RF) in language modeling for Czech LVCSR. We show that the RF approach can be successfully implemented for language modeling of an inflectional language. Performance of word-based and morphological DTs and RFs was evaluated on lecture recognition task. We show that while DTs perform worse than conventional trigram language models (LM), RFs of both kind outperform the latter. WER (up to 3.4% relative) and perplexity (10%) reduction over the trigram model can be gained with morphological RFs. Further improvement is obtained after interpolation of DT and RF LMs with the trigram one (up to 15.6% perplexity and 4.8% WER relative reduction). In this paper we also investigate distribution of morphological feature types chosen for splitting data at different levels of DTs.

This paper addresses the detection of OOV segments in the output of a large vocabulary continuous speech recognition (LVCSR) system. First, standard confidence measures from frame-based word- and phone-posteriors are investigated. Substantial improvement is obtained when posteriors from two systems - strongly constrained (LVCSR) and weakly constrained (phone posterior estimator) are combined. We show that this approach is also suitable for detection of general recognition errors. All results are presented on WSJ task with reduced recognition vocabulary.

This paper presents a systematic study of performance of TempoRAl Patterns (TRAP) based features and their proposed modifications and combinations for speech recognition in noisy environment. The experimental results are obtained on AURORA 2 database with clean training data. We observed large dependency of performance of different TRAP modifications on noise level. Earlier proposed TRAP system modifications help in clean conditions but degrade the system performance in presence of noise. The combination techniques on the other hand can bring large improvement in case of weak noise and degrade only slightly for strong noise cases. The vector concatenation combination technique is improving the system performance up to strong noise.