Speech Analysis/Synthesis/Conversion by Using Sequential Processing

This paper presents a method for speech analysis/synthesis/conversion by using sequential processing. The aims of rhis method are to improve the quality of synthsized speech and to convert the original speech into another speech of different characteristics. We apply the Kalman Filter for estimating the auto-regressive coefficients of \u27time varying AR model with unknown onput (ARUI model)\u27, which we have proposed to improve the conventinal AR model, and we use a band-pass filter for making \u27a guide signal\u27 to extract the pitch period from the residual signal. These signals are utilized to make the driving source signal in speech synthesis. We also use the for speech conversion, such as in pitch and utterance length. Moreover, we show experomentally that this method can analyze/synthesize/convert speech without causing instability by using the smoothed auto-regressive coefficients

Exploring efficient neural architectures for linguistic-acoustic mapping in text-to-speech

Conversion from text to speech relies on the accurate mapping from linguistic to acoustic symbol sequences, for which current practice employs recurrent statistical models such as recurrent neural networks. Despite the good performance of such models (in terms of low distortion in the generated speech), their recursive structure with intermediate affine transformations tends to make them slow to train and to sample from. In this work, we explore two different mechanisms that enhance the operational efficiency of recurrent neural networks, and study their performance–speed trade-off. The first mechanism is based on the quasi-recurrent neural network, where expensive affine transformations are removed from temporal connections and placed only on feed-forward computational directions. The second mechanism includes a module based on the transformer decoder network, designed without recurrent connections but emulating them with attention and positioning codes. Our results show that the proposed decoder networks are competitive in terms of distortion when compared to a recurrent baseline, whilst being significantly faster in terms of CPU and GPU inference time. The best performing model is the one based on the quasi-recurrent mechanism, reaching the same level of naturalness as the recurrent neural network based model with a speedup of 11.2 on CPU and 3.3 on GPU.Peer ReviewedPostprint (published version

Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities