Tingzhi Mao

Genshun Wan, Mengzhi Wang, Tingzhi Mao et al.

The transducer model trained based on sequence-level criterion requires a lot of memory due to the generation of the large probability matrix. We proposed a lightweight transducer model based on frame-level criterion, which uses the results of the CTC forced alignment algorithm to determine the label for each frame. Then the encoder output can be combined with the decoder output at the corresponding time, rather than adding each element output by the encoder to each element output by the decoder as in the transducer. This significantly reduces memory and computation requirements. To address the problem of imbalanced classification caused by excessive blanks in the label, we decouple the blank and non-blank probabilities and truncate the gradient of the blank classifier to the main network. Experiments on the AISHELL-1 demonstrate that this enables the lightweight transducer to achieve similar results to transducer. Additionally, we use richer information to predict the probability of blank, achieving superior results to transducer.

Tingzhi Mao, Yerbolat Khassanov, Van Tung Pham et al.

Automatic speech recognition (ASR) for under-represented named-entity (UR-NE) is challenging due to such named-entities (NE) have insufficient instances and poor contextual coverage in the training data to learn reliable estimates and representations. In this paper, we propose approaches to enriching UR-NEs to improve speech recognition performance. Specifically, our first priority is to ensure those UR-NEs to appear in the word lattice if there is any. To this end, we make exemplar utterances for those UR-NEs according to their categories (e.g. location, person, organization, etc.), ending up with an improved language model (LM) that boosts the UR-NE occurrence in the word lattice. With more UR-NEs appearing in the lattice, we then boost the recognition performance through lattice rescoring methods. We first enrich the representations of UR-NEs in a pre-trained recurrent neural network LM (RNNLM) by borrowing the embedding representations of the rich-represented NEs (RR-NEs), yielding the lattices that statistically favor the UR-NEs. Finally, we directly boost the likelihood scores of the utterances containing UR-NEs and gain further performance improvement.

Haobo Zhang, Tingzhi Mao, Haihua Xu et al.

We report our NTU-AISG Text-to-speech (TTS) entry systems for the Blizzard Challenge 2020 in this paper. There are two TTS tasks in this year's challenge, one is a Mandarin TTS task, the other is a Shanghai dialect TTS task. We have participated both. One of the main challenges is to build TTS systems with low-resource constraints, particularly for the case of Shanghai dialect, of which about three hours data are available to participants. To overcome the constraint, we adopt an average-speaker modeling method. That is, we first employ external Mandarin data to train both End-to-end acoustic model and WaveNet vocoder, then we use Shanghai dialect to tune the acoustic model and WaveNet vocoder respectively. Apart from this, we have no Shanghai dialect lexicon despite syllable transcripts are provided for the training data. Since we are not sure if similar syllable transcripts are provided for the evaluation data during the training stage, we use Mandarin lexicon for Shanghai dialect instead. With the letter, as decomposed from the corresponding Mandarin syllable, as input, though the naturalness and original speaker similarity of the synthesized speech are good, subjective evaluation results indicate the intelligibility of the synthesized speech is deeply undermined for the Shanghai dialect TTS system.

Tingzhi Mao, Yerbolat Khassanov, Van Tung Pham et al.

In this paper, we present a series of complementary approaches to improve the recognition of underrepresented named entities (NE) in hybrid ASR systems without compromising overall word error rate performance. The underrepresented words correspond to rare or out-of-vocabulary (OOV) words in the training data, and thereby can't be modeled reliably. We begin with graphemic lexicon which allows to drop the necessity of phonetic models in hybrid ASR. We study it under different settings and demonstrate its effectiveness in dealing with underrepresented NEs. Next, we study the impact of neural language model (LM) with letter-based features derived to handle infrequent words. After that, we attempt to enrich representations of underrepresented NEs in pretrained neural LM by borrowing the embedding representations of rich-represented words. This let us gain significant performance improvement on underrepresented NE recognition. Finally, we boost the likelihood scores of utterances containing NEs in the word lattices rescored by neural LMs and gain further performance improvement. The combination of the aforementioned approaches improves NE recognition by up to 42% relatively.