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The conventional automatic speech recognition (ASR) systems employ the GMM-HMM for acoustic modeling and the n-gram for language modeling. Over the last decade, the deep feed-forward neural network (DFNN) has almost replaced the GMM in acoustic modeling. The current ASR systems are predominantly based on the DFNN-HMM acoustic model and the n-gram language model (LM). Owing to better long-term context...
Mandarin and Tibetan Lhasa dialect are chosen to be the research objects. Phones sets and corresponding Latin Transformation scheme of Mandarin and Tibetan Lhasa dialect are established respectively. KL distance between two GMMs are studied. GMM-HMM models for phones of two languages are trained on the basis of corpus and pronunciation dictionaries. Phones of Mandarin and Tibetan Lhasa dialect are...
This paper proposes a cascading deep neural network (DNN) structure for speech synthesis system that consists of text-to-bottleneck (TTB) and bottleneck-to-speech (BTS) models. Unlike conventional single structure that requires a large database to find complicated mapping rules between linguistic and acoustic features, the proposed structure is very effective even if the available training database...
Adaptability and controllability are the major advantages of statistical parametric speech synthesis (SPSS) over unit-selection synthesis. Recently, deep neural networks (DNNs) have significantly improved the performance of SPSS. However, current studies are mainly focusing on the training of speaker-dependent DNNs, which generally requires a significant amount of data from a single speaker. In this...
This paper presents a deep neural network (DNN)-based unit selection method for waveform concatenation speech synthesis using frame-sized speech segments. In this method, three DNNs are adopted to calculate target costs and concatenation costs respectively for selecting frame-sized candidate units. The first DNN is built in the same way as the DNN-based statistical parametric speech synthesis, which...
This study compared the perceptions of Chinese sentences conveying the attitudinal contrast of praising and blaming by five groups of subjects (Chinese natives, Japanese L2 learners of Mandarin, French L2 learners of Mandarin, Japanese and French subjects without any Mandarin ability). Context-elicited target sentences conveying praising, blaming or neutral attitude were used as stimuli in the listening...
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 highly-complex transformation compactly, but also take advantage of huge amount of training data. In this...
This paper presents a deep neural network-conditional random field (DNN-CRF) system with multi-view features for sentence unit detection on English broadcast news. We proposed a set of multi-view features extracted from the acoustic, articulatory, and linguistic domains, and used them together in the DNN-CRF model to predict the sentence boundaries. We tested the accuracy of the multi-view features...
Detection of affective states in speech could improve the way users interact with electronic devices. However the analysis of speech at the acoustic level could be not enough to determine the emotion of a user speaking in a realistic scenario. In this paper we analysed the spontaneous speech recordings of the FAU Aibo Corpus at the acoustic and linguistic levels to extract two sets of acoustic and...
For large-vocabulary continuous speech recognition (LVCSR) of highly-inflected languages, selection of an appropriate recognition unit is the first important step. The morpheme-based approach is often adopted because of its high coverage and linguistic properties. But morpheme units are short, often consisting of one or two phonemes, thus they are more likely to be confused in ASR than word units...
Rapidly increasing quantities of multimedia and spoken content today demand fast and accurate retrieval approaches for convenient browsing. The spoken documents with wide variety of different acoustic and linguistic conditions make supervised training of well-matched acoustic/language models very difficult. Unsupervised methods using frame-based dynamic time warping (DTW) require no acoustic/language...
In this paper, we present a new method for video genre identification based on the linguistic content analysis. This approach relies on the analysis of the most frequent words in the video transcriptions provided by an automatic speech recognition system. Experiments are conducted on a corpus composed of cartoons, movies, news, commercials, documentary, sport and music. On this 7-genre identification...
The primary study of this paper is focused on the acoustic module (AM) design in order to improve the performance of Mandarin TTS system. The AM is composed of the prosody generator, the spectrum generator, and the speech synthesizer. The HMM, recurrent neural network (RNN), and PSOLA algorithms are employed to build the AM. Finally, the performance analyses including the speech quality, memory requirement,...
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