485 research outputs found
Unsupervised crosslingual adaptation of tokenisers for spoken language recognition
Phone tokenisers are used in spoken language recognition (SLR) to obtain elementary
phonetic information. We present a study on the use of deep neural
network tokenisers. Unsupervised crosslingual adaptation was performed to
adapt the baseline tokeniser trained on English conversational telephone speech
data to different languages. Two training and adaptation approaches, namely
cross-entropy adaptation and state-level minimum Bayes risk adaptation, were
tested in a bottleneck i-vector and a phonotactic SLR system. The SLR systems
using the tokenisers adapted to different languages were combined using score
fusion, giving 7-18% reduction in minimum detection cost function (minDCF)
compared with the baseline configurations without adapted tokenisers. Analysis
of results showed that the ensemble tokenisers gave diverse representation of
phonemes, thus bringing complementary effects when SLR systems with different
tokenisers were combined. SLR performance was also shown to be related
to the quality of the adapted tokenisers
PHONOTACTIC AND ACOUSTIC LANGUAGE RECOGNITION
Práce pojednává o fonotaktickĂ©m a akustickĂ©m pĹ™Ăstupu pro automatickĂ© rozpoznávánĂ jazyka. Prvnà část práce pojednává o fonotaktickĂ©m pĹ™Ăstupu zaloĹľenĂ©m na vĂ˝skytu fonĂ©movĂ˝ch sekvenci v Ĺ™eÄŤi. NejdĹ™Ăve je prezentován popis vĂ˝voje fonĂ©movĂ©ho rozpoznávaÄŤe jako techniky pro pĹ™epis Ĺ™eÄŤi do sekvence smysluplnĂ˝ch symbolĹŻ. HlavnĂ dĹŻraz je kladen na dobrĂ© natrĂ©novánĂ fonĂ©movĂ©ho rozpoznávaÄŤe a kombinaci vĂ˝sledkĹŻ z nÄ›kolika fonĂ©movĂ˝ch rozpoznávaÄŤĹŻ trĂ©novanĂ˝ch na rĹŻznĂ˝ch jazycĂch (ParalelnĂ fonĂ©movĂ© rozpoznávánĂ následovanĂ© jazykovĂ˝mi modely (PPRLM)). Práce takĂ© pojednává o novĂ© technice anti-modely v PPRLM a studuje pouĹľitĂ fonĂ©movĂ˝ch grafĹŻ mĂsto nejlepšĂho pĹ™episu. Na závÄ›r práce jsou porovnány dva pĹ™Ăstupy modelovánĂ vĂ˝stupu fonĂ©movĂ©ho rozpoznávaÄŤe -- standardnĂ n-gramovĂ© jazykovĂ© modely a binárnĂ rozhodovacĂ stromy. HlavnĂ pĹ™Ănos v akustickĂ©m pĹ™Ăstupu je diskriminativnĂ modelovánĂ cĂlovĂ˝ch modelĹŻ jazykĹŻ a prvnĂ experimenty s kombinacĂ diskriminativnĂho trĂ©novánĂ a na pĹ™ĂznacĂch, kde byl odstranÄ›n vliv kanálu. Práce dále zkoumá rĹŻznĂ© druhy technik fĂşzi akustickĂ©ho a fonotaktickĂ©ho pĹ™Ăstupu. Všechny experimenty jsou provedeny na standardnĂch datech z NIST evaluaci konanĂ© v letech 2003, 2005 a 2007, takĹľe jsou pĹ™Ămo porovnatelnĂ© s vĂ˝sledky ostatnĂch skupin zabĂ˝vajĂcĂch se automatickĂ˝m rozpoznávánĂm jazyka. S fĂşzĂ uvedenĂ˝ch technik jsme posunuli state-of-the-art vĂ˝sledky a dosáhli vynikajĂcĂch vĂ˝sledkĹŻ ve dvou NIST evaluacĂch.This thesis deals with phonotactic and acoustic techniques for automatic language recognition (LRE). The first part of the thesis deals with the phonotactic language recognition based on co-occurrences of phone sequences in speech. A thorough study of phone recognition as tokenization technique for LRE is done, with focus on the amounts of training data for phone recognizer and on the combination of phone recognizers trained on several language (Parallel Phone Recognition followed by Language Model - PPRLM). The thesis also deals with novel technique of anti-models in PPRLM and investigates into using phone lattices instead of strings. The work on phonotactic approach is concluded by a comparison of classical n-gram modeling techniques and binary decision trees. The acoustic LRE was addressed too, with the main focus on discriminative techniques for training target language acoustic models and on initial (but successful) experiments with removing channel dependencies. We have also investigated into the fusion of phonotactic and acoustic approaches. All experiments were performed on standard data from NIST 2003, 2005 and 2007 evaluations so that the results are directly comparable to other laboratories in the LRE community. With the above mentioned techniques, the fused systems defined the state-of-the-art in the LRE field and reached excellent results in NIST evaluations.
Language discrimination by newborns: Teasing apart phonotactic, rhythmic, and intonational cues
Speech rhythm has long been claimed to be a useful bootstrapping cue in the very first steps of language acquisition. Previous studies have suggested that newborn infants do categorize varieties of speech rhythm, as demonstrated by their ability to discriminate between certain languages. However, the existing evidence is not unequivocal: in previous studies, stimuli discriminated by newborns always contained additional speech cues on top of rhythm. Here, we conducted a series of experiments assessing discrimination between Dutch and Japanese by newborn infants, using a speech resynthesis technique to progressively degrade non-rhythmical properties of the sentences. When the stimuli are resynthesized using identical phonemes and artificial intonation contours for the two languages, thereby preserving only their rhythmic and broad phonotactic structure, newborns still seem to be able to discriminate between the two languages, but the effect is weaker than when intonation is present. This leaves open the possibility that the temporal correlation between intonational and rhythmic cues might actually facilitate the processing of speech rhythm
Identification of Non-Linguistic Speech Features
Over the last decade technological advances have been made which enable us to envision real-world applications of speech technologies. It is possible to foresee applications where the spoken query is to be recognized without even prior knowledge of the language being spoken, for example, information centers in public places such as train stations and airports. Other applications may require accurate identification of the speaker for security reasons, including control of access to confidential information or for telephone-based transactions. Ideally, the speaker's identity can be verified continually during the transaction, in a manner completely transparent to the user. With these views in mind, this paper presents a unified approach to identifying non-linguistic speech features from the recorded signal using phone-based acoustic likelihoods. This technique is shown to be effective for text-independent language, sex, and speaker identification and can enable better and more friendly human-machine interaction. With 2s of speech, the language can be identified with better than 99 % accuracy. Error in sex-identification is about 1% on a per-sentence basis, and speaker identification accuracies of 98.5 % on TIMIT (168 speakers) and 99.2 % on BREF (65 speakers), were obtained with one utterance per speaker, and 100 % with 2 utterances for both corpora. An experiment using unsupervised adaptation for speaker identification on the 168 TIMIT speakers had the same identification accuracies obtained with supervised adaptation
Current trends in multilingual speech processing
In this paper, we describe recent work at Idiap Research Institute in the domain of multilingual speech processing and provide some insights into emerging challenges for the research community. Multilingual speech processing has been a topic of ongoing interest to the research community for many years and the field is now receiving renewed interest owing to two strong driving forces. Firstly, technical advances in speech recognition and synthesis are posing new challenges and opportunities to researchers. For example, discriminative features are seeing wide application by the speech recognition community, but additional issues arise when using such features in a multilingual setting. Another example is the apparent convergence of speech recognition and speech synthesis technologies in the form of statistical parametric methodologies. This convergence enables the investigation of new approaches to unified modelling for automatic speech recognition and text-to-speech synthesis (TTS) as well as cross-lingual speaker adaptation for TTS. The second driving force is the impetus being provided by both government and industry for technologies to help break down domestic and international language barriers, these also being barriers to the expansion of policy and commerce. Speech-to-speech and speech-to-text translation are thus emerging as key technologies at the heart of which lies multilingual speech processin
Significance of GMM-UBM based Modelling for Indian Language Identification
AbstractMost of the Indian languages are originated from Devanagari, the script of the Sanskrit language. In-spite of similarity in phoneme sets, every language its own influence on the phonotactic constraints of speech in that language. A modelling technique that is capable of capturing the slightest variations imparted by the language is a pre-requisite for developing a language identification system (LID). Use of Gaussian mixture modelling technique with a large number of mixture components demands a large training data for each language class, which is hard to collect and handle. In this work, phonotactic variations imparted by the different languages are modelled using Gaussian mixture modelling with a universal background model (GMM-UBM) technique. In GMM-UBM based modelling certain amount of data from all the language classes is pooled to develop a universal background model (UBM) and the model is adapted to each class. Spectral features (MFCC) are employed to represent the language specific phonotactic information of speech in different languages. During the present study, LID systems are developed using the speech samples from IITKGP-MLILSC. In this work, performance of the proposed GMM-UBM based LID system is compared with conventional GMM based LID system. An average improvement of 7–8% is observed due to the use of UBM-based modelling of developing a LID system
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Dialect Recognition Using a Phone-GMM-Supervector-Based SVM Kernel
In this paper, we introduce a new approach to dialect recognition which relies on the hypothesis that certain phones are realized differently across dialects. Given a speaker’s utterance, we first obtain the most likely phone sequence using a phone recognizer. We then extract GMM Supervectors for each phone instance. Using these vectors, we design a kernel function that computes the similarities of phones between pairs of utterances. We employ this kernel to train SVM classifiers that estimate posterior probabilities, used during recognition. Testing our approach on four Arabic dialects from 30s cuts, we compare our performance to five approaches: PRLM; GMM-UBM; our own improved version of GMM-UBM which employs fMLLR adaptation; our recent discriminative phonotactic approach; and a state-of-the-art system: SDC-based GMM-UBM discriminatively trained. Our kernel-based technique outperforms all these previous approaches; the overall EER of our system is 4.9%
Acoustic Approaches to Gender and Accent Identification
There has been considerable research on the problems of speaker and language recognition
from samples of speech. A less researched problem is that of accent recognition. Although this
is a similar problem to language identification, di�erent accents of a language exhibit more
fine-grained di�erences between classes than languages. This presents a tougher problem
for traditional classification techniques. In this thesis, we propose and evaluate a number of
techniques for gender and accent classification. These techniques are novel modifications and
extensions to state of the art algorithms, and they result in enhanced performance on gender
and accent recognition.
The first part of the thesis focuses on the problem of gender identification, and presents a
technique that gives improved performance in situations where training and test conditions are
mismatched.
The bulk of this thesis is concerned with the application of the i-Vector technique to accent
identification, which is the most successful approach to acoustic classification to have emerged
in recent years. We show that it is possible to achieve high accuracy accent identification without
reliance on transcriptions and without utilising phoneme recognition algorithms. The thesis
describes various stages in the development of i-Vector based accent classification that improve
the standard approaches usually applied for speaker or language identification, which are
insu�cient. We demonstrate that very good accent identification performance is possible with
acoustic methods by considering di�erent i-Vector projections, frontend parameters, i-Vector
configuration parameters, and an optimised fusion of the resulting i-Vector classifiers we can
obtain from the same data.
We claim to have achieved the best accent identification performance on the test corpus
for acoustic methods, with up to 90% identification rate. This performance is even better than
previously reported acoustic-phonotactic based systems on the same corpus, and is very close
to performance obtained via transcription based accent identification. Finally, we demonstrate
that the utilization of our techniques for speech recognition purposes leads to considerably
lower word error rates.
Keywords: Accent Identification, Gender Identification, Speaker Identification, Gaussian
Mixture Model, Support Vector Machine, i-Vector, Factor Analysis, Feature Extraction, British
English, Prosody, Speech Recognition
Frame-level features conveying phonetic information for language and speaker recognition
150 p.This Thesis, developed in the Software Technologies Working Group of the Departmentof Electricity and Electronics of the University of the Basque Country, focuseson the research eld of spoken language and speaker recognition technologies.More specically, the research carried out studies the design of a set of featuresconveying spectral acoustic and phonotactic information, searches for the optimalfeature extraction parameters, and analyses the integration and usage of the featuresin language recognition systems, and the complementarity of these approacheswith regard to state-of-the-art systems. The study reveals that systems trained onthe proposed set of features, denoted as Phone Log-Likelihood Ratios (PLLRs), arehighly competitive, outperforming in several benchmarks other state-of-the-art systems.Moreover, PLLR-based systems also provide complementary information withregard to other phonotactic and acoustic approaches, which makes them suitable infusions to improve the overall performance of spoken language recognition systems.The usage of this features is also studied in speaker recognition tasks. In this context,the results attained by the approaches based on PLLR features are not as remarkableas the ones of systems based on standard acoustic features, but they still providecomplementary information that can be used to enhance the overall performance ofthe speaker recognition systems
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