21 accepted papers
The efficacy and robustness of Ordinal Regression (OR) in assessing speech pronunciation for language learning at phrase level has been shown before. However, for assessing phoneme pronunciation, we need to: 1. collect human scoring annotations for phoneme tokens of a short duration (60-70 ms); 2. t…
Many mispronunciation detection and diagnosis (MD&D) research approaches try to exploit both the acoustic and linguistic features as input. Yet the improvement of the performance is limited, partially due to the shortage of large amount annotated training data at the phoneme level. Phonetic embeddin…
FastSpeech, as a feed-forward transformer based TTS, can avoid the slow serial, autoregressive inference to generate the target mel-spectrogram in a parallel way. As a non-autoregressive TTS, the latency and computation load in inference is shifted from vocoder to transformer where the efficiency is…
A trajectory tiling based, hybrid TTS is revisited in this study for improving its synthesis performance. A combination of Transformer encoder and RNN based decoder architecture where two-level, at both word and Chinese phonetic alphabet letter levels, linguistic representation is exploited to gener…
Sentence level pronunciation assessment is important for Computer Assisted Language Learning (CALL). Traditional speech pronunciation assessment, based on the Goodness of Pronunciation (GOP) algorithm, has some weakness in assessing a speech utterance: 1) Phoneme GOP scores cannot be easily translat…
Mean opinion score (MOS) is a popular subjective metric to assess the quality of synthesized speech, and usually involves multiple human judges to evaluate each speech utterance. To reduce the labor cost in MOS test, multiple methods have been proposed to automatically predict MOS scores. To our kno…
This paper presents a speech BERT model to extract embedded prosody information in speech segments for improving the prosody of synthesized speech in neural text-to-speech (TTS). As a pre-trained model, it can learn prosody attributes from a large amount of speech data, which can utilize more data t…
A frame-unit-selection based voice conversion system proposed earlier by us is revisited here to enhance its performance in both speech naturalness and speaker similarity. Speaker independent, bilingual (Mandarin Chinese and American English) deep neural net (DNN) acoustic model’s output, frame-leve…
In this paper, we propose an improved LPCNet vocoder using a linear prediction (LP)-structured mixture density network (MDN). The recently proposed LPCNet vocoder has successfully achieved high-quality and lightweight speech synthesis systems by combining a vocal tract LP filter with a WaveRNN-based…
Recent advances of neural TTS have made "human parity" synthesized speech possible when a large amount of studio-quality training data from a voice talent is available. However, with only limited, casual recordings from an ordinary speaker, human-like TTS is still a big challenge, in addition to oth…
Despite significant advancements of deep learning on separating speech sources mixed in a single channel, same gender speaker mix, i.e., male-male or female-female, is still more difficult to separate than the case of opposite gender mix. In this study, we propose a pitch-aware speech separation app…
A second language (L2) learner usually cannot speak L2 well in both pronunciations and forming-of-words. Hence his/her L2 speech cannot be well recognized by a recognizer trained with native data. Domain adversarial training (DAT), capable of reducing the acoustic mismatch between training and testi…
Automatic assessment of a language learner's speech fluency is highly desirable for language education, e.g. for English as a Second Language (ESL) learning. In this paper, we formulate the fluency assessment as a problem of Ordinal Regression with Anchored Reference Samples (ORARS), where the fluen…
In this paper, given the speaker bottleneck feature vectors extracted with speaker discriminant neural networks, we focus on using the sequential speaker characteristics for text-dependent speaker verification. In each evaluation trial, speaker supervectors are used as the representations of the seq…
We propose a Kullback-Leibler divergence (KLD) and deep neural net (DNN) based approach to cross-lingual TTS (CL-TTS) training. A speaker independent DNN (SI-DNN) ASR is used to equalize the speaker difference between a source speaker in L1 and a reference speaker in L2. Two speaker dependent GMM-HM…
We have successfully proposed to use multi-speaker modelling in DNN-based TTS synthesis for improved voice quality with limited available data from a speaker. In this paper, we propose a new speaker and language factorized DNN, where speaker-specific layers are used for multi-speaker modelling, and…
Multi-speaker TTS trained with a general DNN has outperformed individually modelled baseline [1]. Multi-speaker DNN takes advantages of larger amount of training data from multiple speakers to find robust transformations in the hidden layers and covers more speaker variability in the output regressi…
This paper presents a new approach to cross-lingual voice transformation in HMM-based TTS with only the recordings from two monolingual speakers in different languages (e.g. Mandarin and English). We aim to synthesize one speaker's speech in the other language. We regard the spectral space of any sp…
In DNN-based TTS synthesis, DNNs hidden layers can be viewed as deep transformation for linguistic features and the output layers as representation of acoustic space to regress the transformed linguistic features to acoustic parameters. The deep-layered architectures of DNN can not only represent hi…
Long short-term memory (LSTM) is a specific recurrent neural network (RNN) architecture that is designed to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we propose to use deep bidirectional LSTM (BLSTM) for audio/visual modeling in…
The current state of the art TTS synthesis can produce synthesized speech with highly decent quality if rich segmental and suprasegmental information are given. However, some suprasegmental features, e.g., Tone and Break (TOBI), are time consuming due to being manually labeled with a high inconsiste…