Privacy-sensitive audio features for conversational speech processing

Investigating privacy-sensitive features for speech detection in multiparty conversations

We investigate four different privacy-sensitive features, namely energy, zero crossing rate, spectral flatness, and kurtosis, for speech detection in multiparty conversations. We liken this scenario to a meeting room and define our datasets and annotations accordingly. The temporal context of these features is modeled. With no temporal context, energy is the best performing single feature. But ...

Articulatory features for conversational speech recognition

While the overall performance of speech recognition systems continues to improve, they still show a dramatic increase in word error rate when tested on different speaking styles, i.e. when speakers for example want to make an important point during a meeting and change from sloppy speech to clear speech. Today’s speech recognizers are therefore not robust with respect to speaking style, althoug...

Speech Processing for Audio Indexing

Audio-visual Speech Processing

Speech is inherently bimodal, relying on cues from the acoustic and visual speech modalities for perception. The McGurk effect demonstrates that when humans are presented with conflicting acoustic and visual stimuli, the perceived sound may not exist in either modality. This effect has formed the basis for modelling the complementary nature of acoustic and visual speech by encapsulating them in...

A Framework for Conversational Arabic Speech Long Audio Alignment

We propose a framework for long audio alignment for conversational Arabic speech. Accurate alignments help in many speech processing tasks such as audio indexing, speech recognizer acoustic model (AM) training, audio summarizing and retrieving, etc. In this work, we have collected more than 1400 hours of conversational Arabic besides the corresponding non-aligned text transcriptions. Automatic ...