Neural Correlates of Auditory Perceptual Awareness and Release from Informational Masking Recorded Directly from Human Cortex: A Case Study.

In complex acoustic environments, even salient supra-threshold sounds sometimes go unperceived, a phenomenon known as informational masking. The neural basis of informational masking (and its release) has not been well-characterized, particularly outside auditory cortex. We combined electrocorticography in a neurosurgical patient undergoing invasive epilepsy monitoring with trial-by-trial perceptual reports of isochronous target-tone streams embedded in random multi-tone maskers. Awareness of such masker-embedded target streams was associated with a focal negativity between 100 and 200 ms and high-gamma activity (HGA) between 50 and 250 ms (both in auditory cortex on the posterolateral superior temporal gyrus) as well as a broad P3b-like potential (between ~300 and 600 ms) with generators in ventrolateral frontal and lateral temporal cortex. Unperceived target tones elicited drastically reduced versions of such responses, if at all. While it remains unclear whether these responses reflect conscious perception, itself, as opposed to pre- or post-perceptual processing, the results suggest that conscious perception of target sounds in complex listening environments may engage diverse neural mechanisms in distributed brain areas

Continuous Multi-Band Speech Recognition using Bayesian Networks

Colloque avec actes et comité de lecture. internationale.International audienceUsing the Bayesian networks framework, we present a new multi-band approach for continuous speech recognition. This new approach has the advantage to overcome all the limitations of the standard multi-band techniques. Moreover, it leads to a higher fidelity speech modeling than HMMs. We provide a preliminary evaluation of the performance of our new approach on a connected digits recognition task

Observations on Multi-Band Asynchrony in Distant Speech Recordings

Whenever the speech signal is captured by a microphone distant from the user, the acoustic response of the room introduces significant distortions. To remove these distortions from the signal, solutions exist that greatly improve the ASR performance (what was said?), such as dereverberation or beamforming. It may seem natural to apply those signal-level methods in the context of speaker clustering (who spoke when?) with distant microphones, for example when annotating a meeting recording for enhanced browsing experience. Unfortunately, on a corpus of real meeting recordings, it appeared that neither dereverberation nor beamforming gave any improvement on the speaker clustering task. The present technical report constitutes a first attempt to explain this failure, through a cross-correlation analysis between close-talking and distant microphone signals. The various frequency bands of the speech spectrum appear to become desynchronized when the speaker is 1 or 2 meters away from the microphone. Further directions of research are suggested to model this desynchronization

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Pushing the envelope: Evaluating speech rhythm with different envelope extraction techniques

The amplitude of the speech signal varies over time, and the speech envelope is an attempt to characterise this variation in the form of an acoustic feature. Although tacitly assumed, the similarity between the speech envelope-derived time series and that of phonetic objects (e.g., vowels) remains empirically unestablished. The current paper, therefore, evaluates several speech envelope extraction techniques, such as the Hilbert transform, by comparing different acoustic landmarks (e.g., peaks in the speech envelope) with manual phonetic annotation in a naturalistic and diverse dataset. Joint speech tasks are also introduced to determine which acoustic landmarks are most closely coordinated when voices are aligned. Finally, the acoustic landmarks are evaluated as predictors for the temporal characterisation of speaking style using classification tasks. The landmark that performed most closely to annotated vowel onsets was peaks in the first derivative of a human audition-informed envelope, consistent with converging evidence from neural and behavioural data. However, differences also emerged based on language and speaking style. Overall, the results show that both the choice of speech envelope extraction technique and the form of speech under study affect how sensitive an engineered feature is at capturing aspects of speech rhythm, such as the timing of vowels