12 accepted papers
For mispronunciation detection and diagnosis (MDD), nowadays approaches generally treat the phonemes in correct and mispronunciations as the same despite the fact they may actually carry different characteristics. Furthermore, serious data imbalance issue between correct and mispronunciation in data…
By highlighting the focus of an utterance to draw attention, emphasis in speech interaction plays an important role for speaker intention expressing and understanding. Therefore, emphatic speech synthesis draws increasing interest in the text-to-speech (TTS) area. For emphatic speech synthesis, thre…
In speech understanding, perceptual computing is widely used to quantify the perceptual properties. Previous researches on perceptual computing mainly focused on the level of phonemes (i.e. consonants and vowels). However, perceptual measurement in the level of syllables is also needed in scenarios…
Bidirectional long short-term memory (BLSTM) recurrent neural network (RNN) has achieved state-of-the-art performance in many sequence processing problems given its capability in capturing contextual information. However, for languages with limited amount of training data, it is still difficult to o…
Recurrent neural networks (RNNs) and their bidirectional long short term memory (BLSTM) variants are powerful sequence modelling approaches. Their inherently strong ability in capturing long range temporal dependencies allow BLSTM-RNN speech synthesis systems to produce higher quality and smoother s…
Music Dynamic Emotion Prediction is a challenging and significant task. In this paper, We adopt the dimensional valence-arousal (V-A) emotion model to represent the dynamic emotion in music. Considering the high context correlation among the music feature sequence and the advantage of Bidirectional…
Bidirectional long short-term memory (BLSTM) based speech synthesis has shown great potential in improving the quality of the synthetic speech. However, for low-resource languages, it is difficult to obtain a high quality BLSTM model. BLSTM based speech synthesis can be viewed as a transformation be…
Speech is bimodal in nature. There are close correlations between the acoustic speech signals and the visual gestures such as lip movements, facial expressions and head motions. For speech driven talking avatar, how to derive more representative acoustic features from which to predict more accurate…
Question detection is of importance for many speech applications. Only parts of the speech utterances can provide useful clues for question detection. Previous work of question detection using acoustic features in Mandarin conversation is weak in capturing such proper time context information, which…
Dynamic music emotion prediction is to recognize the continuous emotion contained in music, and has various applications. In recent years, dynamic music emotion recognition is widely studied, while the inside structure of the emotion in music remains unclear. We conduct a data observation based on t…
To solve the acoustic-to-articulatory inversion problem, this paper proposes a deep bidirectional long short term memory recurrent neural network and a deep recurrent mixture density network. The articulatory parameters of the current frame may have correlations with the acoustic features many frame…
This paper investigates the incorporation of hidden Markov model (HMM) based emphatic speech synthesis for audio exaggeration into an audio-visual speech synthesis framework for the corrective feedback in computer-aided pronunciation training (CAPT). To improve the voice quality of the synthetic emp…