In this paper we present AhoTransf, a tool that enables analysis, visualization, modification and synthesis of speech. AhoTransf integrates a speech signal analysis model with a graphical user interface to allow visualization and modification of the parameters of the model. The synthesis capability allows hearing the modified signal thus providing a quick way to understand the perceptual effect of the changes in the parameters of the model. The speech analysis/synthesis algorithm is based in the Multiband Excitation technique, but uses a novel phase information representation the Relative Phase Shift (RPS’s). With this representation, not only the amplitudes but also the phases of the harmonic components of the speech signal reveal their structured patterns in the visualization tool. AhoTransf is modularly conceived so that it can be used with different harmonic speech models.

The LTSE-VAD is one of the best known algorithms for voice activity detection. In this paper we present a modified version of this algorithm, that makes the VAD decision not taking into account account the estimated background noise level, but the signal to noise ratio (SNR). This makes the algorithm robust not only to noise level changes, but also to signal level changes. We compare the modified algorithm with the original one, and with three other standard VAD systems. The results show that the modified version gets the lowest silence misclassification rate, while maintaining a reasonably low speech misclassification rate. As a result, this algorithm is more suitable for identification tasks, such as speaker or emotion recognition, where silence misclassification can be very harmful. A series of automatic emotion identification experiments are also carried out, proving that the modified version of the algorithm helps increasing the correct emotion classification rate.

Speaker identification and verification systems have a poor performance when model training is done in one language while the testing is done in another. This situation is not unusual in multilingual environments, where people should be able to access the system in any language he or she prefers in each moment, without noticing a performance drop. In this work we study the possibility of using features derived from prosodic parameters in order to reinforce the language robustness of these systems. First the features’ properties in terms of language and session variability are studied, predicting an increase in the language robustness when frame-wise intonation and energy values are combined with traditional MFCC features. The experimental results confirm that these features provide an improvement in the speaker recognition rates under language-mismatch conditions. The whole study is carried out in the Basque Country, a bilingual region in which Basque and Spanish languages co-exist.

This paper describes the evaluation process of an emotional speech database recorded for standard Basque, in order to determine its adequacy for the analysis of emotional models and its use in speech synthesis. The corpus consists of seven hundred semantically neutral sentences that were recorded for the Big Six emotions and neutral style, by two professional actors. The test results show that every emotion is readily recognized far above chance level for both speakers. Therefore the database is a valid linguistic resource for the research and development purposes it was designed for.