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Attention-Based Wavenet-Ctc for Tibetan Multi-Dialect Multitask Speech Recognition

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Attention-Based Wavenet-Ctc for Tibetan Multi-Dialect Multitask Speech Recognition ATTENTION-BASED WAVENET-CTC FOR TIBETAN MULTI-DIALECT MULTITASK SPEECH RECOGNITION Jianjian Yue Yue Zhao (correspongding author) Xiaona Xu School of Information and Engineering School of Information and Engineering School of Information and Engineering Minzu University of China Minzu University of China Minzu University of China Beijing, China Beijing, China Beijing, China [email protected] [email protected] [email protected] Licheng Wu Xiali Li Bo Liu School of Information and Engineering School of Information and Engineering School of Information and Engineering Minzu University of China Minzu University of China Minzu University of China Beijing, China Beijing, China Beijing, China [email protected] [email protected] [email protected] Qiang Ji Department of Electrical, Computer, and Systems Engineering Rensselaer Polytechnic Institute Troy NY 12180-3590, USA [email protected] Abstract—To date, Tibetan has very limited resources for knowledge [14]. Hence, it is of great significance to make the conventional automatic speech recognition. It lacks of enough process faster and easier using an end-to-end model. The work data, sub-word units, lexicons, and word inventories for some in [14] adopted the listen, attend, and spell (LAS) model for 7 dialects. In this paper, we present an end-to-end model, English dialects, and it showed good performance. Similar attention-based WaveNet-CTC, to perform simultaneous work in [15] with multitask end-to-end learning for 9 Indian Tibetan speech content recognition and dialect identification. languages obtained the largest improvement by conditioning This model avoids processing the pronunciation dictionary and the encoder on the speech language identity. These studies word segmentation for new dialects while allowing for training showed that the attention-based encoder-decoder of end-to- of multi-dialect speech content recognition and dialect end model can contribute to handling the variations between identification in a single model. We introduced the attention different dialects by learning and optimizing a single neural mechanism into WaveNet to capture the context information and relations of discontinuous speech frames among different network. dialects and tasks. And the dialect information is used in the However, Connectionist Temporal Classification (CTC) output for multitask learning. The experimental results show for end-to-end has its advantage of training simplicity, and is that attention-based WaveNet-CTC has better performance one of the most popular methods used in speech recognition compared with WaveNet-CTC and task-specific models for the [16,17]. The work in [18] directly incorporated attention multi-dialect multitask speech recognition. modeling within CTC framework to address high word error rates (WER) for a character-based end-to-end. But for Tibetan Keywords—multi-dialect speech recognition, multitask learning, WaveNet-CTC model, attention mechanism, Tibetan multi-dialect speech recognition, Tibetan character is a two- language dimensional planar character, which is written in Tibetan letters from left to right, but there is a vertical superposition in I. INTRODUCTION syllables, so a word-based CTC is more suitable for modelling. In our work, we try to introduce attention Tibetan is one of the most widely used minority languages mechanism in encoder for CTC-based end-to-end model in China. It is also used in parts of India, Bhutan, and Nepal. [19,20]. The attention is used in encoder to capture the context During the long-term development of the Tibetan language, information and relations of discontinuous speech frames different dialects have been formed. The language is divided among different dialects for distinguishing dialect content and into three major dialects in China: Ü-Tsang, Kham, and identity. Amdo. Tibetan dialects are pronounced very differently in different regions, but the written characters are unified across WaveNet is a deep generative model with very large regions. Since the Lhasa of Ü-Tsang dialect is standard receptive fields, it can model the long-term dependency on Tibetan speech, there is more research on its linguistics, speech data [21]. It has been efficiently applied for speech speech recognition, and corpus establishment than on those of generation and text-to-speech. A generative model can capture other dialects [1–8]. the underlying data distribution as well as the mechanisms to generate data; we believe that such abilities are crucial for Recently, the field of speech recognition has been in the shared representation across speech data from different midst of a paradigm shift: end-to-end neural networks are dialects. Further, WaveNet can give the prediction distribution challenging the dominance of hidden Markov models as a core for speech data conditioned on all previous input, so dialect technology [9-13]. End-to-end automatic speech recognition information can be used as additional label outputs during has more advantages than conventional DNN/HMM systems, training to improve recognition performance in different especially for low-resource languages, because it avoids the dialects. So in this paper, we used WaveNet as encoder for need for linguistic resources like dictionaries and phonetic CTC-based end-to-end model and incorporated attention 978-1-7281-6926-2/20/$31.00 ©2020 IEEE modeling within WaveNet for improving Tibetan multi- Inspired by the idea of the multitask processing dialect multitask speech recognition. mechanism of the brain, many researchers have conducted work on the application of a multitask framework to speech II. RELATED WORK recognition. The work in [32] used the multitask framework Deep learning has had a huge impact on speech to conduct joint training of multiple low-resource languages, recognition since Hinton et al. first introduced deep neural exploring the universal phoneme set as a secondary task to networks (DNNs) into this field. Researchers have applied improve the effect of the phoneme model of each language. various neural network models, such as recurrent neural The work in [33] integrated speaker recognition and speech networks (RNNs), long short-term memory (LSTM), and recognition into a multitask learning framework using a CNNs, to the field to greatly improve system performance recursive structure, which used the output of one task as [9,10,12]. However, these models divide the whole speech additional information for another task to supervise individual recognition system into separate components, such as acoustic task learning. In [34], the researchers conducted joint learning models and language models; each component is trained of accent recognizer and multi-dialect acoustic models to separately, and then they are combined to perform recognition improve the performance of acoustic models using the same tasks. There are some obvious shortcomings to this approach: features. All these works demonstrate the effectiveness of The creation of language resources is very time-consuming in multi-task mechanism. the pipeline of a speech recognition system, especially for So, it is very significant to establish an accurate Tibetan low-resource languages; expert knowledge is required to multi-dialect multitask recognition system using a large construct a pronunciation dictionary, which brings implicit amount of Lhasa-Ü-Tsang speech data and limited amount of difficulty for building a speech recognition system; there is no other dialect data based on end-to-end model. It can not only unified optimization function to optimize the components as a relieve the burdensome data requirements, but also quickly whole. expand the existing recognition model to other target Based on this, the researchers have proposed direct languages, which can accelerate the application of Tibetan speech-to-label mapping, namely, an end-to-end model [19, speech recognition technology. 22-25]. There are two major types of end-to-end architectures for ASR: The first one is based on connectionist temporal classification (CTC) [16], which aims to map the III. DATA AND METHODS unsegmented data to the modeling unit sequence, sum all the probabilities of the legal sequences, and take the one of largest A. Data probability as the output sequence [17,19]. The other Our experimental data is from an open and free Tibetan architecture is an attention-based encoder–decoder model. multi-lingual speech data set TIBMD@MUC, which can be Compared with the standard encoder–decoder model, the downloaded from [35]. attention mechanism can assign different weights to each part of input X and extract more related and important information, The text corpus consists of two parts. One is 1396 spoken leading to more accurate predictions without bringing language sentences selected from the book “Tibetan Spoken calculation and storage overload [26-28]. In the work [29], Language” [36] written by La Bazelen, and the other part is attention was directly incorporated in CTC network for small collected to 8,000 sentences from online news, electronic output unit, such as character-based CTC speech recognition. novels and poetry of Tibetan on internet. All text corpuses In this paper, we will try to use attention mechanism as an include a total of 3497 Tibetan syllables. independent component to training a word-based CTC end-to- There are 114 recorders who were from Lhasa City in end model for multi-dialect multitask speech recognition. Tibet, Yushu City in Qinghai Province, Changdu City in Tibet In Tibetan speech recognition, researchers have paid
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