Hidden Markov Acoustic Modeling With Bootstrap and Restructuring for Low-Resourced Languages

Cui, Xiaodong; Xue, Jian; Chen, Xin; Olsen, Peder A.; Dognin, Pierre; Chaudhari, Upendra V.; Hershey, John R.; Zhou, Bowen

doi:10.1109/tasl.2012.2199982

Cited by 14 publications

(10 citation statements)

References 35 publications

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“…We consider a query as OOV if it contains at least one OOV term. The KWS results are produced for six different ASR systems: (1) GMM, the baseline GMM/HMM system which is a discriminatively trained, speaker-adaptively trained acoustic model; (2) BSRS, a Bootstrap and restructuring model [20] in which the original training data is randomly re-sampled to produce multiple subsets and the resulting models are aggregated at the state level to produce a large, composite model; (3) CU-HTK, a TANDEM HMM system from Cambridge University using cross-word, stateclustered, triphone models trained with MPE, fMPE, and speakeradaptive training. For efficiency, the MLP features were incorporated in the same fashion as [21]; (4) MLP, a multi-layer perceptron model [22] which is a GMM-based ASR system that uses neuralnetwork features; (5) NN-GMM, a speaker-adaptively and discriminatively trained GMM/HMM system from RWTH Aachen University using bottle-neck neural network features [23] and a 4-gram Kneser-Ney LM with optimized discounting parameters [24] using a modified version of the RWTH open source decoder [25]; and (6) DBN, a deep belief network hybrid model [26,27] with discriminative pertraining, frame-level cross-entropy training and state-level minimum Bayes risk sequence training.…”

Section: Methodsmentioning

confidence: 99%

System combination and score normalization for spoken term detection

Mamou

Cui

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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Spoken content in languages of emerging importance needs to be searchable to provide access to the underlying information. In this paper, we investigate the problem of extending data fusion methodologies from Information Retrieval for Spoken Term Detection on low-resource languages in the framework of the IARPA Babel program. We describe a number of alternative methods improving keyword search performance. We apply these methods to Cantonese, a language that presents some new issues in terms of reduced resources and shorter query lengths. First, we show score normalization methodology that improves in average by 20% keyword search performance. Second, we show that properly combining the outputs of diverse ASR systems performs 14% better than the best normalized ASR system.

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Section: Methodsmentioning

confidence: 99%

System combination and score normalization for spoken term detection

Mamou

Cui

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…8,16,20, and 24, i.e., the ensemble performance increased with the mixture size. This implies that the ensemble model is able to take advantage of the reduced bias in the overfit base models by reducing the variance that accompanies the overfit.…”

Section: Bias and Variancementioning

confidence: 96%

Ensemble Learning Approaches in Speech Recognition

Zhao

Xue

Chen

2014

Speech and Audio Processing for Coding, Enhancement and Recognition

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An overview is made on the ensemble learning efforts that have emerged in automatic speech recognition in recent years. The approaches that are based on different machine learning techniques and target various levels and components of speech recognition are described, and their effectiveness is discussed in terms of the direct performance measure of word error rate and the indirect measures of classification margin, diversity, as well as bias and variance. In addition, methods on reducing storage and computation costs of ensemble models for practical deployments of speech recognition systems are discussed. Ensemble learning for speech recognition has been largely fruitful, and it is expected to continue progress along with the advances in machine learning, speech and language modeling, as well as computing technology.

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“…In such circumstance authors tested a state-mapping approach [20], where the main task is to find the state similarities of average voice-language-dependent acoustic models [17]. Also, frame mapping and Gaussian component mapping have been explored in [21,22] and [23], respectively.…”

Section: Related Workmentioning

confidence: 99%

Towards automatic cross-lingual acoustic modelling applied to HMM-based speech synthesis for under-resourced languages

Justin

Žibert

2016

Automatika

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Original scientific paperNowadays Human Computer Interaction (HCI) can also be achieved with voice user interfaces (VUIs). To enable devices to communicate with humans by speech in the user's own language, low-cost language portability is often discussed and analysed. One of the most time-consuming parts for the language-adaptation process of VUIcapable applications is the target-language speech-data acquisition. Such data is further used in the development of VUIs subsystems, especially of speech-recognition and speech-production systems. The tempting idea to bypass a long-term process of data acquisition is considering the design and development of an automatic algorithms, which can extract the similar target-language acoustic from different language speech databases. This paper focus on the cross-lingual phoneme mapping between an under-resourced and a well-resourced language. It proposes a novel automatic phoneme-mapping technique that is adopted from the speaker-verification field. Such a phoneme mapping is further used in the development of the HMM-based speech-synthesis system for the under-resourced language. The synthesised utterances are evaluated with a subjective evaluation and compared by the expert knowledge cross-language method against to the baseline speech synthesis based just from the under-resourced data. The results reveals, that combining data from well-resourced and under-resourced language with the use of the proposed phoneme-mapping technique, can improve the quality of under-resourced language speech synthesis.Key words: Voice user interfaces, Human language technologies, HMM-based speech synthesis, Cross-language synthesis, Under-resourced languages, UBM-MAP-GMM phoneme mapping Primjena automatskog medujezičnog akustičnog modeliranja na HMM sintezu govora za oskudne jezične baze. U današnje vrijeme interakcijačovjeka i računala (HCI) može se ostvariti i putem govornih sučelja (VUIs). Da bi se omogućila komunikacija uredaja i korisnika putem govora na vlastitom korisnikovom jeziku, cesto se raspravlja i analizira o jeftinom rješenju prijevoda govora na različite jezike. Jedan od vremenski najzahtjevnijih dijelova procesa prilagodbe jezika za aplikacije koje podržavaju VUI je prikupljanje govornih podataka za ciljani jezik. Ovakvi podaci dalje se koriste za razvoj VUI podsustava, posebice za prepoznavanje i produkciju govora. Primamljiva ideja za izbjegavanje dugotrajnog postupka prikupljanja podataka jeste razmatranje sinteze i razvoja automatskih algoritama koji su sposobni izvesti slična akustična svojstva za ciljani jezik iz postojećih baza različitih jezika. Ovaj rad fokusiran je na povezivanje medujezičnih fonema izmedu oskudnih i bogatih jezičnih baza. Predložena je nova tehnika automatskog povezivanja fonema, usvojena i prilagodena iz područja govorne autentikacije. Ovakvo povezivanje fonema kasnije se koristi za razvoj sustava za sintezu govora zasnovanom na HMM-u za manje poznate jezike. Načinjene govorne izjave ocijenjene su subjektivnim pristupom kroz usporedbu medujezičnih m...

show abstract

Hidden Markov Acoustic Modeling With Bootstrap and Restructuring for Low-Resourced Languages

Cited by 14 publications

References 35 publications

System combination and score normalization for spoken term detection

System combination and score normalization for spoken term detection

Ensemble Learning Approaches in Speech Recognition

Towards automatic cross-lingual acoustic modelling applied to HMM-based speech synthesis for under-resourced languages

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