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Distant speech recognition is being revolutionized by deep learning, that has contributed to significantly outperform previous HMM-GMM systems. A key aspect behind the rapid rise and success of DNNs is their ability to better manage large time contexts. With this regard, asymmetric context windows that embed more past than future frames have been recently used with feed-forward neural networks. This...
Despite the remarkable progress recently made in distant speech recognition, state-of-the-art technology still suffers from a lack of robustness, especially when adverse acoustic conditions characterized by non-stationary noises and reverberation are met.
Improving distant speech recognition is a crucial step towards flexible human-machine interfaces. Current technology, however, still exhibits a lack of robustness, especially when adverse acoustic conditions are met. Despite the significant progress made in the last years on both speech enhancement and speech recognition, one potential limitation of state-of-the-art technology lies in composing modules...
This paper introduces the contents and the possible usage of the DIRHA-ENGLISH multi-microphone corpus, recently realized under the EC DIRHA project. The reference scenario is a domestic environment equipped with a large number of microphones and microphone arrays distributed in space. The corpus is composed of both real and simulated material, and it includes 12 US and 12 UK English native speakers...
This paper describes a new corpus of multi-channel audio data designed to study and develop distant-speech recognition systems able to cope with known interfering sounds propagating in an environment. The corpus consists of both real and simulated signals and of a corresponding detailed annotation. An extensive set of speech recognition experiments was conducted using three different Acoustic Echo...
To improve speech recognition performance, a combination between TANDEM and bottleneck Deep Neural Networks (DNN) is investigated. In particular, exploiting a feature combination performed by means of a multi-stream hierarchical processing, we show a performance improvement by combining the same input features processed by different neural networks. The experiments are based on the spontaneous telephone...
Domestic environments are particularly challenging for distant speech recognition and audio processing in general. Reverberation, background noise and interfering sources, as well as the propagation of acoustic events across adjacent rooms, critically degrade the performance of standard speech processing algorithms. The DIRHA EU project addresses the development of distant-speech interaction with...
Audio-based multimedia retrieval tasks may identify semantic information in audio streams, i.e., audio concepts (such as music, laughter, or a revving engine). Conventional Gaussian-Mixture-Models have had some success in classifying a reduced set of audio concepts. However, multi-class classification can benefit from context window analysis and the discriminating power of deeper architectures. Although...
This paper refers to a voice-enabled smart-home scenario, for which contaminated speech is produced to train a distant-speech recognition system. The impulse response measurement process is investigated, with a specific focus on its impact on speech recognition performance. Experimental results, related to a phone-loop and to a word-loop task, show that a significant change of performance is obtained...
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