Why has (reasonably accurate) Automatic Speech Recognition been so hard to achieve?

Hidden Markov models (HMMs) have been successfully applied to automatic speech recognition for more than 35 years in spite of the fact that a key HMM assumption -- the statistical independence of frames -- is obviously violated by speech data. In fact, this data/model mismatch has inspired many attempts to modify or replace HMMs with alternative models that are better able to take into account the statistical dependence of frames. However it is fair to say that in 2010 the HMM is the consensus model of choice for speech recognition and that HMMs are at the heart of both commercially available products and contemporary research systems. In this paper we present a preliminary exploration aimed at understanding how speech data depart from HMMs and what effect this departure has on the accuracy of HMM-based speech recognition. Our analysis uses standard diagnostic tools from the field of statistics -- hypothesis testing, simulation and resampling -- which are rarely used in the field of speech recognition. Our main result, obtained by novel manipulations of real and resampled data, demonstrates that real data have statistical dependency and that this dependency is responsible for significant numbers of recognition errors. We also demonstrate, using simulation and resampling, that if we `remove' the statistical dependency from data, then the resulting recognition error rates become negligible. Taken together, these results suggest that a better understanding of the structure of the statistical dependency in speech data is a crucial first step towards improving HMM-based speech recognition

Self-Supervised Vision-Based Detection of the Active Speaker as Support for Socially-Aware Language Acquisition

This paper presents a self-supervised method for visual detection of the active speaker in a multi-person spoken interaction scenario. Active speaker detection is a fundamental prerequisite for any artificial cognitive system attempting to acquire language in social settings. The proposed method is intended to complement the acoustic detection of the active speaker, thus improving the system robustness in noisy conditions. The method can detect an arbitrary number of possibly overlapping active speakers based exclusively on visual information about their face. Furthermore, the method does not rely on external annotations, thus complying with cognitive development. Instead, the method uses information from the auditory modality to support learning in the visual domain. This paper reports an extensive evaluation of the proposed method using a large multi-person face-to-face interaction dataset. The results show good performance in a speaker dependent setting. However, in a speaker independent setting the proposed method yields a significantly lower performance. We believe that the proposed method represents an essential component of any artificial cognitive system or robotic platform engaging in social interactions.Comment: 10 pages, IEEE Transactions on Cognitive and Developmental System

Language Identification Using Visual Features

Automatic visual language identification (VLID) is the technology of using information derived from the visual appearance and movement of the speech articulators to iden- tify the language being spoken, without the use of any audio information. This technique for language identification (LID) is useful in situations in which conventional audio processing is ineffective (very noisy environments), or impossible (no audio signal is available). Research in this field is also beneficial in the related field of automatic lip-reading. This paper introduces several methods for visual language identification (VLID). They are based upon audio LID techniques, which exploit language phonology and phonotactics to discriminate languages. We show that VLID is possible in a speaker-dependent mode by discrimi- nating different languages spoken by an individual, and we then extend the technique to speaker-independent operation, taking pains to ensure that discrimination is not due to artefacts, either visual (e.g. skin-tone) or audio (e.g. rate of speaking). Although the low accuracy of visual speech recognition currently limits the performance of VLID, we can obtain an error-rate of < 10% in discriminating between Arabic and English on 19 speakers and using about 30s of visual speech

Prosodic Event Recognition using Convolutional Neural Networks with Context Information

This paper demonstrates the potential of convolutional neural networks (CNN) for detecting and classifying prosodic events on words, specifically pitch accents and phrase boundary tones, from frame-based acoustic features. Typical approaches use not only feature representations of the word in question but also its surrounding context. We show that adding position features indicating the current word benefits the CNN. In addition, this paper discusses the generalization from a speaker-dependent modelling approach to a speaker-independent setup. The proposed method is simple and efficient and yields strong results not only in speaker-dependent but also speaker-independent cases.Comment: Interspeech 2017 4 pages, 1 figur