Language Identification Using Visual Features

Automatic visual language identification (VLID) is the technology of using information derived from the visual appearance and movement of the speech articulators to iden- tify the language being spoken, without the use of any audio information. This technique for language identification (LID) is useful in situations in which conventional audio processing is ineffective (very noisy environments), or impossible (no audio signal is available). Research in this field is also beneficial in the related field of automatic lip-reading. This paper introduces several methods for visual language identification (VLID). They are based upon audio LID techniques, which exploit language phonology and phonotactics to discriminate languages. We show that VLID is possible in a speaker-dependent mode by discrimi- nating different languages spoken by an individual, and we then extend the technique to speaker-independent operation, taking pains to ensure that discrimination is not due to artefacts, either visual (e.g. skin-tone) or audio (e.g. rate of speaking). Although the low accuracy of visual speech recognition currently limits the performance of VLID, we can obtain an error-rate of < 10% in discriminating between Arabic and English on 19 speakers and using about 30s of visual speech

ASR decoding in a computational model of human word recognition

This paper investigates the interaction between acoustic scores and symbolic mismatch penalties in multi-pass speech decoding techniques that are based on the creation of a segment graph followed by a lexical search. The interaction between acoustic and symbolic mismatches determines to a large extent the structure of the search space of these multipass approaches. The background of this study is a recently developed computational model of human word recognition, called SpeM. SpeM is able to simulate human word recognition data and is built as a multi-pass speech decoder. Here, we focus on unravelling the structure of the search space that is used in SpeM and similar decoding strategies. Finally, we elaborate on the close relation between distances in this search space, and distance measures in search spaces that are based on a combination of acoustic and phonetic features

SVMs for Automatic Speech Recognition: a Survey

Hidden Markov Models (HMMs) are, undoubtedly, the most employed core technique for Automatic Speech Recognition (ASR). Nevertheless, we are still far from achieving high-performance ASR systems. Some alternative approaches, most of them based on Artificial Neural Networks (ANNs), were proposed during the late eighties and early nineties. Some of them tackled the ASR problem using predictive ANNs, while others proposed hybrid HMM/ANN systems. However, despite some achievements, nowadays, the preponderance of Markov Models is a fact. During the last decade, however, a new tool appeared in the field of machine learning that has proved to be able to cope with hard classification problems in several fields of application: the Support Vector Machines (SVMs). The SVMs are effective discriminative classifiers with several outstanding characteristics, namely: their solution is that with maximum margin; they are capable to deal with samples of a very higher dimensionality; and their convergence to the minimum of the associated cost function is guaranteed. These characteristics have made SVMs very popular and successful. In this chapter we discuss their strengths and weakness in the ASR context and make a review of the current state-of-the-art techniques. We organize the contributions in two parts: isolated-word recognition and continuous speech recognition. Within the first part we review several techniques to produce the fixed-dimension vectors needed for original SVMs. Afterwards we explore more sophisticated techniques based on the use of kernels capable to deal with sequences of different length. Among them is the DTAK kernel, simple and effective, which rescues an old technique of speech recognition: Dynamic Time Warping (DTW). Within the second part, we describe some recent approaches to tackle more complex tasks like connected digit recognition or continuous speech recognition using SVMs. Finally we draw some conclusions and outline several ongoing lines of research

Phonetic Temporal Neural Model for Language Identification

Deep neural models, particularly the LSTM-RNN model, have shown great potential for language identification (LID). However, the use of phonetic information has been largely overlooked by most existing neural LID methods, although this information has been used very successfully in conventional phonetic LID systems. We present a phonetic temporal neural model for LID, which is an LSTM-RNN LID system that accepts phonetic features produced by a phone-discriminative DNN as the input, rather than raw acoustic features. This new model is similar to traditional phonetic LID methods, but the phonetic knowledge here is much richer: it is at the frame level and involves compacted information of all phones. Our experiments conducted on the Babel database and the AP16-OLR database demonstrate that the temporal phonetic neural approach is very effective, and significantly outperforms existing acoustic neural models. It also outperforms the conventional i-vector approach on short utterances and in noisy conditions.Comment: Submitted to TASL

Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification

There are a number of studies about extraction of bottleneck (BN) features from deep neural networks (DNNs)trained to discriminate speakers, pass-phrases and triphone states for improving the performance of text-dependent speaker verification (TD-SV). However, a moderate success has been achieved. A recent study [1] presented a time contrastive learning (TCL) concept to explore the non-stationarity of brain signals for classification of brain states. Speech signals have similar non-stationarity property, and TCL further has the advantage of having no need for labeled data. We therefore present a TCL based BN feature extraction method. The method uniformly partitions each speech utterance in a training dataset into a predefined number of multi-frame segments. Each segment in an utterance corresponds to one class, and class labels are shared across utterances. DNNs are then trained to discriminate all speech frames among the classes to exploit the temporal structure of speech. In addition, we propose a segment-based unsupervised clustering algorithm to re-assign class labels to the segments. TD-SV experiments were conducted on the RedDots challenge database. The TCL-DNNs were trained using speech data of fixed pass-phrases that were excluded from the TD-SV evaluation set, so the learned features can be considered phrase-independent. We compare the performance of the proposed TCL bottleneck (BN) feature with those of short-time cepstral features and BN features extracted from DNNs discriminating speakers, pass-phrases, speaker+pass-phrase, as well as monophones whose labels and boundaries are generated by three different automatic speech recognition (ASR) systems. Experimental results show that the proposed TCL-BN outperforms cepstral features and speaker+pass-phrase discriminant BN features, and its performance is on par with those of ASR derived BN features. Moreover,....Comment: Copyright (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Temporal Parameters of Spontaneous Speech in Forensic Speaker Identification in Case of Language Mismatch: Serbian as L1 and English as L2

Celem badania jest analiza możliwości identyfikacji mówcy kryminalistycznego i sądowego podczas zadawania pytań w różnych językach, z wykorzystaniem parametrów temporalnych. (wskaźnik artykulcji, wskaźnik mowy, stopień niezdecydowania, odsetek pauz, średnia czas trwania pauzy). Korpus obejmuje 10 mówców kobiet z Serbii, które znają język angielksi na poziomie zaawwansowanym. Patrametry są badane z wykorzystaniem beayesowskiego wzoru wskaźnika prawdopodobieństwa w 40 parach tcyh samych mówców i w 230 parach różnych mówców, z uwzględnieniem szacunku wskaźnika błędu, równiego wskaźnika błędu i Całościowego Wskaźnika Prawdopodobieństwa. badanie ma charakter pionierski w zakresie językoznawstwa sądowego i kryminalistycznego por1) ónawczego w parze jezyka serbskiego i angielskiego, podobnie, jak analiza parametrów temporalnych mówców bilingwalnych. Dalsze badania inny skoncentrować się na porównaniu języków z rytmem akcentowym i z rytmem sylabicznym. The purpose of the research is to examine the possibility of forensic speaker identification if question and suspect sample are in different languages using temporal parameters (articulation rate, speaking rate, degree of hesitancy, percentage of pauses, average pause duration). The corpus includes 10 female native speakers of Serbian who are proficient in English. The parameters are tested using Bayesian likelihood ratio formula in 40 same-speaker and 360 different-speaker pairs, including estimation of error rates, equal error rates and Overall Likelihood Ratio. One-way ANOVA is performed to determine whether inter-speaker variability is higher than intra- speaker variability across languages. The most successful discriminant is degree of hesitancy with ER of 42.5%/28%, (EER: 33%), followed by average pause duration with ER 35%/45.56%, (EER: 40%). Although the research features a closed-set comparison, which is not very common in forensic reality, the results are still relevant for forensic phoneticians working on criminal cases or as expert witnesses. This study pioneers in forensically comparing Serbian and English as well as in forensically testing temporal parameters on bilingual speakers. Further research should focus on comparing two stress-timed or two syllable-timed languages to test whether they will be more comparable in terms of temporal aspects of speech.

Identyfikacja parametrów czasowych mowy spontanicznej mówców kryminalistycznych w przypadku niedopasowania językowego: język serbski jako L1 i język angielski jako L2

The purpose of the research is to examine the possibility of forensic speaker identification if question and suspect sample are in different languages using temporal parameters (articulation rate, speaking rate, degree of hesitancy, percentage of pauses, average pause duration). The corpus includes 10 female native speakers of Serbian who are proficient in English. The parameters are tested using Bayesian likelihood ratio formula in 40 same-speaker and 360 different-speaker pairs, including estimation of error rates, equal error rates and Overall Likelihood Ratio. One-way ANOVA is performed to determine whether inter-speaker variability is higher than intra- speaker variability across languages. The most successful discriminant is degree of hesitancy with ER of 42.5%/28%, (EER: 33%), followed by average pause duration with ER 35%/45.56%, (EER: 40%). Although the research features a closed-set comparison, which is not very common in forensic reality, the results are still relevant for forensic phoneticians working on criminal cases or as expert witnesses. This study pioneers in forensically comparing Serbian and English as well as in forensically testing temporal parameters on bilingual speakers. Further research should focus on comparing two stress-timed or two syllable-timed languages to test whether they will be more comparable in terms of temporal aspects of speech. Celem badania jest analiza możliwości identyfikacji mówcy kryminalistycznego i sądowego podczas zadawania pytań w różnych językach, z wykorzystaniem parametrów temporalnych. (wskaźnik artykulcji, wskaźnik mowy, stopień niezdecydowania, odsetek pauz, średnia czas trwania pauzy). Korpus obejmuje 10 mówców kobiet z Serbii, które znają język angielksi na poziomie zaawwansowanym. Patrametry są badane z wykorzystaniem beayesowskiego wzoru wskaźnika prawdopodobieństwa w 40 parach tcyh samych mówców i w 230 parach różnych mówców, z uwzględnieniem szacunku wskaźnika błędu, równiego wskaźnika błędu i Całościowego Wskaźnika Prawdopodobieństwa. badanie ma charakter pionierski w zakresie językoznawstwa sądowego i kryminalistycznego por1) ónawczego w parze jezyka serbskiego i angielskiego, podobnie, jak analiza parametrów temporalnych mówców bilingwalnych. Dalsze badania inny skoncentrować się na porównaniu języków z rytmem akcentowym i z rytmem sylabicznym.

Real-time decoding of question-and-answer speech dialogue using human cortical activity.

Natural communication often occurs in dialogue, differentially engaging auditory and sensorimotor brain regions during listening and speaking. However, previous attempts to decode speech directly from the human brain typically consider listening or speaking tasks in isolation. Here, human participants listened to questions and responded aloud with answers while we used high-density electrocorticography (ECoG) recordings to detect when they heard or said an utterance and to then decode the utterance's identity. Because certain answers were only plausible responses to certain questions, we could dynamically update the prior probabilities of each answer using the decoded question likelihoods as context. We decode produced and perceived utterances with accuracy rates as high as 61% and 76%, respectively (chance is 7% and 20%). Contextual integration of decoded question likelihoods significantly improves answer decoding. These results demonstrate real-time decoding of speech in an interactive, conversational setting, which has important implications for patients who are unable to communicate