Exploiting correlogram structure for robust speech recognition with multiple speech sources

This paper addresses the problem of separating and recognising speech in a monaural acoustic mixture with the presence of competing speech sources. The proposed system treats sound source separation and speech recognition as tightly coupled processes. In the first stage sound source separation is performed in the correlogram domain. For periodic sounds, the correlogram exhibits symmetric tree-like structures whose stems are located on the delay that corresponds to multiple pitch periods. These pitch-related structures are exploited in the study to group spectral components at each time frame. Local pitch estimates are then computed for each spectral group and are used to form simultaneous pitch tracks for temporal integration. These processes segregate a spectral representation of the acoustic mixture into several time-frequency regions such that the energy in each region is likely to have originated from a single periodic sound source. The identified time-frequency regions, together with the spectral representation, are employed by a `speech fragment decoder' which employs `missing data' techniques with clean speech models to simultaneously search for the acoustic evidence that best matches model sequences. The paper presents evaluations based on artificially mixed simultaneous speech utterances. A coherence-measuring experiment is first reported which quantifies the consistency of the identified fragments with a single source. The system is then evaluated in a speech recognition task and compared to a conventional fragment generation approach. Results show that the proposed system produces more coherent fragments over different conditions, which results in significantly better recognition accuracy

Analysis of very low quality speech for mask-based enhancement

The complexity of the speech enhancement problem has motivated many different solutions. However, most techniques address situations in which the target speech is fully intelligible and the background noise energy is low in comparison with that of the speech. Thus while current enhancement algorithms can improve the perceived quality, the intelligibility of the speech is not increased significantly and may even be reduced. Recent research shows that intelligibility of very noisy speech can be improved by the use of a binary mask, in which a binary weight is applied to each time-frequency bin of the input spectrogram. There are several alternative goals for the binary mask estimator, based either on the Signal-to-Noise Ratio (SNR) of each time-frequency bin or on the speech signal characteristics alone. Our approach to the binary mask estimation problem aims to preserve the important speech cues independently of the noise present by identifying time-frequency regions that contain significant speech energy. The speech power spectrum varies greatly for different types of speech sound. The energy of voiced speech sounds is concentrated in the harmonics of the fundamental frequency while that of unvoiced sounds is, in contrast, distributed across a broad range of frequencies. To identify the presence of speech energy in a noisy speech signal we have therefore developed two detection algorithms. The first is a robust algorithm that identifies voiced speech segments and estimates their fundamental frequency. The second detects the presence of sibilants and estimates their energy distribution. In addition, we have developed a robust algorithm to estimate the active level of the speech. The outputs of these algorithms are combined with other features estimated from the noisy speech to form the input to a classifier which estimates a mask that accurately reflects the time-frequency distribution of speech energy even at low SNR levels. We evaluate a mask-based speech enhancer on a range of speech and noise signals and demonstrate a consistent increase in an objective intelligibility measure with respect to noisy speech.Open Acces

An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony

In this thesis we investigate the applicability and utility of Monaural Sound Source Separation (MSSS) via Nonnegative Matrix Factorization (NMF) for various problems related to audio for hands-free telephony. We first investigate MSSS via NMF as an alternative acoustic echo reduction approach to existing approaches such as Acoustic Echo Cancellation (AEC). To this end, we present the single-channel acoustic echo problem as an MSSS problem, in which the objective is to extract the users signal from a mixture also containing acoustic echo and noise. To perform separation, NMF is used to decompose the near-end microphone signal onto the union of two nonnegative bases in the magnitude Short Time Fourier Transform domain. One of these bases is for the spectral energy of the acoustic echo signal, and is formed from the in- coming far-end user’s speech, while the other basis is for the spectral energy of the near-end speaker, and is trained with speech data a priori. In comparison to AEC, the speaker extraction approach obviates Double-Talk Detection (DTD), and is demonstrated to attain its maximal echo mitigation performance immediately upon initiation and to maintain that performance during and after room changes for similar computational requirements. Speaker extraction is also shown to introduce distortion of the near-end speech signal during double-talk, which is quantified by means of a speech distortion measure and compared to that of AEC. Subsequently, we address Double-Talk Detection (DTD) for block-based AEC algorithms. We propose a novel block-based DTD algorithm that uses the available signals and the estimate of the echo signal that is produced by NMF-based speaker extraction to compute a suitably normalized correlation-based decision variable, which is compared to a fixed threshold to decide on doubletalk. Using a standard evaluation technique, the proposed algorithm is shown to have comparable detection performance to an existing conventional block-based DTD algorithm. It is also demonstrated to inherit the room change insensitivity of speaker extraction, with the proposed DTD algorithm generating minimal false doubletalk indications upon initiation and in response to room changes in comparison to the existing conventional DTD. We also show that this property allows its paired AEC to converge at a rate close to the optimum. Another focus of this thesis is the problem of inverting a single measurement of a non- minimum phase Room Impulse Response (RIR). We describe the process by which percep- tually detrimental all-pass phase distortion arises in reverberant speech filtered by the inverse of the minimum phase component of the RIR; in short, such distortion arises from inverting the magnitude response of the high-Q maximum phase zeros of the RIR. We then propose two novel partial inversion schemes that precisely mitigate this distortion. One of these schemes employs NMF-based MSSS to separate the all-pass phase distortion from the target speech in the magnitude STFT domain, while the other approach modifies the inverse minimum phase filter such that the magnitude response of the maximum phase zeros of the RIR is not fully compensated. Subjective listening tests reveal that the proposed schemes generally produce better quality output speech than a comparable inversion technique

Deep Learning for Distant Speech Recognition

Deep learning is an emerging technology that is considered one of the most promising directions for reaching higher levels of artificial intelligence. Among the other achievements, building computers that understand speech represents a crucial leap towards intelligent machines. Despite the great efforts of the past decades, however, a natural and robust human-machine speech interaction still appears to be out of reach, especially when users interact with a distant microphone in noisy and reverberant environments. The latter disturbances severely hamper the intelligibility of a speech signal, making Distant Speech Recognition (DSR) one of the major open challenges in the field. This thesis addresses the latter scenario and proposes some novel techniques, architectures, and algorithms to improve the robustness of distant-talking acoustic models. We first elaborate on methodologies for realistic data contamination, with a particular emphasis on DNN training with simulated data. We then investigate on approaches for better exploiting speech contexts, proposing some original methodologies for both feed-forward and recurrent neural networks. Lastly, inspired by the idea that cooperation across different DNNs could be the key for counteracting the harmful effects of noise and reverberation, we propose a novel deep learning paradigm called network of deep neural networks. The analysis of the original concepts were based on extensive experimental validations conducted on both real and simulated data, considering different corpora, microphone configurations, environments, noisy conditions, and ASR tasks.Comment: PhD Thesis Unitn, 201