We propose a simple new representation for the FFT spectrum tailored to statistical parametric speech synthesis. It consists of four feature streams that describe magnitude, phase and fundamental frequency using real numbers. The proposed feature extraction method does not attempt to decompose the speech structure (e.g., into source+filter or harmonics+noise). By avoiding the simplifications inherent in decomposition, we can dramatically reduce the "phasiness" and "buzziness" typical of most vocoders. The method uses simple and computationally cheap operations and can operate at a lower frame rate than the 200 frames-per-second typical in many systems. It avoids heuristics and methods requiring approximate or iterative solutions, including phase unwrapping.Two DNN-based acoustic models were built -from male and female speech data -using the Merlin toolkit. Subjective comparisons were made with a state-of-the-art baseline, using the STRAIGHT vocoder. In all variants tested, and for both male and female voices, the proposed method substantially outperformed the baseline. We provide source code to enable our complete system to be replicated.

In this paper we study the use of neural networks in bandwidth extension algorithms (BWE). The aim of such algorithms is to extend the frequency range of speech signals generating a large band signal from the corresponding narrowband signal, in order to improve its hearing quality. Based upon the presumption that the information contained in the available bandlimited speech and in the missing frequency components are correlated, we develop an algorithm which maps this relation using a neural network. The results were then analyzed and compared.

This paper analyzes a) how often listeners interpret the emotional content of an utterance incorrectly when listening to vocoded or natural speech in adverse conditions; b) which noise conditions cause the most misperceptions; and c) which group of listeners misinterpret emotions the most. The long-term goal is to construct new emotional speech synthesizers that adapt to the environment and to the listener. We performed a large-scale listening test where over 400 listeners between the ages of 21 and 72 assessed natural and vocoded acted emotional speech stimuli. The stimuli had been artificially degraded using a room impulse response recorded in a car and various in-car noise types recorded in a real car. Experimental results show that the recognition rates for emotions and perceived emotional strength degrade as signal-to-noise ratio decreases. Interestingly, misperceptions seem to be more pronounced for negative and lowarousal emotions such as calmness or anger, while positive emotions such as happiness appear to be more robust to noise. An ANOVA analysis of listener meta-data further revealed that gender and age also influenced results, with elderly male listeners most likely to incorrectly identify emotions.

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Intelligibility of speech in noise becomes lower as the listeners age increases, even when no apparent hearing impairment is present. The losses are, however, different depending on the nature of the noise and the characteristics of the voice. In this paper we investigate the effect that age, noise type and speaking style have on the intelligibility of speech reproduced by car loudspeakers. Using a binaural mannequin we recorded a variety of voices and speaking styles played from the audio system of a car while driving in different conditions. We used this material to create a listening test where participants were asked to transcribe what they could hear and recruited groups of young and older adults to take part in it. We found that intelligibility scores of older participants were lower for the competing speaker and background music conditions. Results also indicate that clear and Lombard speech was more intelligible than plain speech for both age groups. A mixed effect model revealed that the largest effect was the noise condition, followed by sentence type, speaking style, voice, age group and pure tone average.