4,911 research outputs found
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
Model-based Parametric Prosody Synthesis with Deep Neural Network
Conventional statistical parametric speech synthesis (SPSS) captures only frame-wise acoustic observations and computes probability densities at HMM state level to obtain statistical acoustic models combined with decision trees, which is therefore a purely statistical data-driven approach without explicit integration of any articulatory mechanisms found in speech production research. The present study explores an alternative paradigm, namely, model-based parametric prosody synthesis (MPPS), which integrates dynamic mechanisms of human speech production as a core component of F0 generation. In this paradigm, contextual variations in prosody are processed in two separate yet integrated stages: linguistic to motor, and motor to acoustic. Here the motor model is target approximation (TA), which generates syllable-sized F0 contours with only three motor parameters that are associated to linguistic functions. In this study, we simulate this two-stage process by linking the TA model to a deep neural network (DNN), which learns the “linguistic-motor” mapping given the “motor-acoustic” mapping provided by TA-based syllable-wise F0 production. The proposed prosody modeling system outperforms the HMM-based baseline system in both objective and subjective evaluations
Standard Yorùbá context dependent tone identification using Multi-Class Support Vector Machine (MSVM)
Most state-of-the-art large vocabulary continuous speech recognition systems employ context dependent (CD) phone units, however, the CD phone units are not efficient in capturing long-term spectral dependencies of tone in most tone languages. The Standard Yorùbá (SY) is a language composed of syllable with tones and requires different method for the acoustic modeling. In this paper, a context dependent tone acoustic model was developed. Tone unit is assumed as syllables, amplitude magnified difference function (AMDF) was used to derive the utterance wide F contour, followed by automatic syllabification and tri-syllable forced alignment with speech phonetization alignment and syllabification SPPAS tool. For classification of the context dependent (CD) tone, slope and intercept of F values were extracted from each segmented unit. Supervised clustering scheme was utilized to partition CD tri-tone based on category and normalized based on some statistics to derive the acoustic feature vectors. Multi-class support vector machine (MSVM) was used for tri-tone training. From the experimental results, it was observed that the word recognition accuracy obtained from the MSVM tri-tone system based on dynamic programming tone embedded features was comparable with phone features. A best parameter tuning was obtained for 10-fold cross validation and overall accuracy was 97.5678%. In term of word error rate (WER), the MSVM CD tri-tone system outperforms the hidden Markov model tri-phone system with WER of 44.47%.Keywords: Syllabification, Standard Yorùbá, Context Dependent Tone, Tri-tone Recognitio
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.
Automatic Speech Recognition for Low-resource Languages and Accents Using Multilingual and Crosslingual Information
This thesis explores methods to rapidly bootstrap automatic speech recognition systems for languages, which lack resources for speech and language processing. We focus on finding approaches which allow using data from multiple languages to improve the performance for those languages on different levels, such as feature extraction, acoustic modeling and language modeling. Under application aspects, this thesis also includes research work on non-native and Code-Switching speech
Automatic Pronunciation Assessment -- A Review
Pronunciation assessment and its application in computer-aided pronunciation
training (CAPT) have seen impressive progress in recent years. With the rapid
growth in language processing and deep learning over the past few years, there
is a need for an updated review. In this paper, we review methods employed in
pronunciation assessment for both phonemic and prosodic. We categorize the main
challenges observed in prominent research trends, and highlight existing
limitations, and available resources. This is followed by a discussion of the
remaining challenges and possible directions for future work.Comment: 9 pages, accepted to EMNLP Finding
Incorporating pitch features for tone modeling in automatic recognition of Mandarin Chinese
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 53-56).Tone plays a fundamental role in Mandarin Chinese, as it plays a lexical role in determining the meanings of words in spoken Mandarin. For example, these two sentences ... (I like horses) and ... (I like to scold) differ only in the tone carried by the last syllable. Thus, the inclusion of tone-related information through analysis of pitch data should improve the performance of automatic speech recognition (ASR) systems on Mandarin Chinese. The focus of this thesis is to improve the performance of a non-tonal automatic speech recognition (ASR) system on a Mandarin Chinese corpus by implementing modifications to the system code to incorporate pitch features. We compile and format a Mandarin Chinese broadcast new corpus for use with the ASR system, and implement a pitch feature extraction algorithm. Additionally, we investigate two algorithms for incorporating pitch features in Mandarin Chinese speech recognition. Firstly, we build and test a baseline tonal ASR system with embedded tone modeling by concatenating the cepstral and pitch feature vectors for use as the input to our phonetic model (a Hidden Markov Model, or HMM). We find that our embedded tone modeling algorithm does improve performance on Mandarin Chinese, showing that including tonal information is in fact contributive for Mandarin Chinese speech recognition. Secondly, we implement and test the effectiveness of HMM-based multistream models.by Karen Lingyun Chu.M.Eng
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