Beamforming is crucial for hands-free mobile terminals and voice-enabled automated home environments based on distant-speech interaction to mitigate causes of system degradation, e.g., interfering noise sources or competing speakers. This paper presents an adaptation of the most common state-of-the-art broadband beamformers to uniform circular arrays, such that competing speakers are attenuated sufficiently for distant speech recognition. As a result, a new beamformer is presented. Finally, the speech quality of the beamformers’ enhanced signals is evaluated with different objective speech quality measures and a word recognizer as a measure for the attenuation of competing speakers.

Communication between humans deeply relies on the capability of expressing and recognizing feelings. For this reason, research on human-machine interaction needs to focus on the recognition and simulation of emotional states, prerequisite of which is the collection of affective corpora. Currently available datasets still represent a bottleneck for the difficulties arising during the acquisition and labeling of affective data. In this work, we present a new audio-visual corpus for possibly the two most important modalities used by humans to communicate their emotional states, namely speech and facial expression in the form of dense dynamic 3-D face geometries. We acquire high-quality data by working in a controlled environment and resort to video clips to induce affective states. The annotation of the speech signal includes: transcription of the corpus text into the phonological representation, accurate phone segmentation, fundamental frequency extraction, and signal intensity estimation of the speech signals. We employ a real-time 3-D scanner to acquire dense dynamic facial geometries and track the faces throughout the sequences, achieving full spatial and temporal correspondences. The corpus is a valuable tool for applications like affective visual speech synthesis or view-independent facial expression recognition.

Within a polyglot text-to-speech synthesis system, the generation of an adequate prosody for mixed-lingual texts, sentences, or even words, requires a polyglot prosody model that is able to seamlessly switch between languages and that applies the same voice for all languages. This paper presents the first polyglot prosody model that fulfills these requirements and that is constructed from independent monolingual prosody models. A perceptual evaluation showed that the synthetic polyglot prosody of about 82 % of German and French mixed-lingual test sentences cannot be distinguished from natural polyglot prosody. Index Terms: mixed-lingual, polyglot, speech synthesis, prosody control, neural networks, ensemble models

In text-to-speech synthesis systems, the quality of the predicted prosody contours influences quality and naturalness of synthetic speech. This paper presents a new statistical model for prosody control that combines an ensemble learning technique using neural networks as base learners with feature relevance determination. This weighted neural network ensemble model was applied for both, phone duration modeling and fundamental frequency modeling. A comparison with state-of-the-art prosody models based on classification and regression trees (CART), multivariate adaptive regression splines (MARS), or artificial neural networks (ANN), shows a 12 % improvement compared to the best duration model and a 24 % improvement compared to the best F0 model. The neural network ensemble model also outperforms another, recently presented ensemble model based on gradient tree boosting. Index Terms: speech synthesis, prosody control, neural networks, ensemble models

An automatic system for segmenting speech signals used for the training of statistical prosody models is presented. Starting from a canonical transcription, the system simultaneously delivers an accurate phonetic segmentation and the matched phonetic transcription indicating pronunciation variants. Although the system is HMM-based, it uses only the speech signals of the prosody database which typically consists of a few hundred sentences with some 30 minutes total duration. Initial phone HMMs are generated with flat-start training using the canonical transcriptions of the sentences. Then iterative Viterbi search for best-matching pronunciation variants and HMM retraining is applied until convergence is attained.

Polyglot text-to-speech synthesis, i.e. the synthesis of sentences containing one or more inclusions from other languages, primarily depends on an accurate morphosyntactic analyzer for such mixed-lingual texts. From the output of this analyzer, the pronunciation can be derived by means of phonological transformations which are language-specific and depend on various contexts. In this paper a new rule formalism for such phonological transformations is presented, which complies also with the requirements of the mixed-lingual situation.

Text-to-speech (TTS) synthesis is more and more confronted with the language mixing phenomenon. An important step towards the solution of this problem and thus towards a socalled polyglot TTS system is an analysis component for mixedlingual texts. In this paper it is shown how such an analyzer can be realized for a set of languages, starting from a corresponding set of monolingual analyzers which are based on DCGs and chart parsing.