End-to-End-Based Tibetan Multitask Speech Recognition

Zhao, Yue; Yue, Jianjian; Xu, Xiaona; Wu, Licheng; Li, Xiali

doi:10.1109/access.2019.2952406

Cited by 17 publications

(6 citation statements)

References 28 publications

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“…It was seen that the journals "IEEE Transaction Speech Audio Processing" and "International Journal of Speech and Technology" given the highest number of papers in the study. [62], [63], [64], [65], [66], [67], [68], [69], [70], [44], [71], [72], [73], [74], [75], [76], [77], [78], [79], [80], [47], [81], [82], [83], [84], [85], [53], [86], [87], [5] Conferences 33 45.2 [88], [63], [89], [90], [91], [92], [93], [94], [95], [96], [97], [98], [99], [100], [22], [101], [102], [103], [104],…”

Section: A Rq1 Aimed To Find the Various Type Of Research Papers Usementioning

confidence: 99%

Acoustic Modeling in Speech Recognition: A Systematic Review

Bhatt¹,

Jain²,

Dev³

2020

IJACSA

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The paper presents a systematic review of acoustic modeling (AM) techniques in speech recognition(SR). Acoustic modeling establishes a relationship between acoustic information and language construct in SR. Over the past decades, researchers presented studies addressing specific concerns in AM. However, all previous research works lack a systematic and comprehensive review of acoustic modeling issues. A systematic review is introduced to understand the acoustic modeling issues in speech recognition. This paper provides an extensive and comprehensive inspection of various researches that have been performed since 1984. The extensive investigation and analysis into AM was performed by getting the relevant data from 73 research works chose after the screening process between the years from 1984 to 2020. The systematic review process was divided into different parts to investigate acoustic modeling issues. Main issues in acoustic modeling such as feature extraction techniques, acoustic modeling units, speech corpora, classification methods, different tools used, language issues applied, and evaluation parameters were investigated. This study helps the reader to understand various acoustic modeling issues with comprehensive details. The research outcomes presented in this study depict research trends and shed light on new research topics in AM. The result of this review can be used to build a better speech recognition system by choosing a suitable acoustic modeling construct in SR.

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Section: A Rq1 Aimed To Find the Various Type Of Research Papers Usementioning

confidence: 99%

Acoustic Modeling in Speech Recognition: A Systematic Review

Bhatt¹,

Jain²,

Dev³

2020

IJACSA

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“…In 2019, Song Wang of Northwest University for Nationalities used the method of combining long short-term memory networks and connection timing classification to carry out end-to-end acoustic modeling and speech recognition of the Lhasa dialect within the U-Tsang dialect [6]. Yue Zhao and others at the Central University for Nationalities used a framework consisting of end-to-end speech recognition and the WaveNetconnectionist temporal classification\1 (CTC) method to train a multitask system that can complete dialect recognition, speech recognition, and speaker recognition tasks simultaneously [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Research on Tibetan Speech Recognition Based on the Am-do Dialect

Khysru¹,

Wei²,

Dang³

2022

Computers, Materials &Amp; Continua

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In China, Tibetan is usually divided into three major dialects: the Am-do, Khams and Lhasa dialects. The Am-do dialect evolved from ancient Tibetan and is a local variant of modern Tibetan. Although this dialect has its own specific historical and social conditions and development, there have been different degrees of communication with other ethnic groups, but all the abovementioned dialects developed from the same language: Tibetan. This paper uses the particularity of Tibetan suffixes in pronunciation and proposes a lexicon for the Am-do language, which optimizes the problems existing in previous research. Audio data of the Am-do dialect are expanded by data augmentation technology combining noise and reverberation, and the morphological characteristics and characteristics of the Tibetan language are further considered. According to the particularity of Tibetan grammar, grammatical features are used to optimize grammatical relationships and are combined with a language model, and the Am-do dialect is scored and rescored. Experimental results show that compared with the baseline, our proposed new lexicon and data augmentation technology yields a relative increase of approximately 3% in character error rates (CERs) and a relative increase of 3%-19% in the recognition rate of acoustic models and language models.

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“…However, in our work and [12], the WaveNet model is used for the generation of waveform sample with the input of predicted Mel spectrogram for speech synthesis. In the work [24] about speech recognition, the WaveNet model is used for the generation of text sequence and the input is MFCC features. e work [12] achieved the speech synthesis for Tibetan Lhasa-Ü-Tsang by using end-to-end model.…”

Section: Introductionmentioning

confidence: 99%

End‐to‐End Speech Synthesis for Tibetan Multidialect

Zhao

et al. 2021

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The research on Tibetan speech synthesis technology has been mainly focusing on single dialect, and thus there is a lack of research on Tibetan multidialect speech synthesis technology. This paper presents an end-to-end Tibetan multidialect speech synthesis model to realize a speech synthesis system which can be used to synthesize different Tibetan dialects. Firstly, Wylie transliteration scheme is used to convert the Tibetan text into the corresponding Latin letters, which effectively reduces the size of training corpus and the workload of front-end text processing. Secondly, a shared feature prediction network with a cyclic sequence-to-sequence structure is built, which maps the Latin transliteration vector of Tibetan character to Mel spectrograms and learns the relevant features of multidialect speech data. Thirdly, two dialect-specific WaveNet vocoders are combined with the feature prediction network, which synthesizes the Mel spectrum of Lhasa-Ü-Tsang and Amdo pastoral dialect into time-domain waveform, respectively. The model avoids using a large number of Tibetan dialect expertise for processing some time-consuming tasks, such as phonetic analysis and phonological annotation. Additionally, it can directly synthesize Lhasa-Ü-Tsang and Amdo pastoral speech on the existing text annotation. The experimental results show that the synthesized speech of Lhasa-Ü-Tsang and Amdo pastoral dialect based on our proposed method has better clarity and naturalness than the Tibetan monolingual model.

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End-to-End-Based Tibetan Multitask Speech Recognition

Cited by 17 publications

References 28 publications

Acoustic Modeling in Speech Recognition: A Systematic Review

Acoustic Modeling in Speech Recognition: A Systematic Review

Research on Tibetan Speech Recognition Based on the Am-do Dialect

End‐to‐End Speech Synthesis for Tibetan Multidialect

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