Effects of Lombard Reflex on the Performance of Deep-Learning-Based Audio-Visual Speech Enhancement Systems

Humans tend to change their way of speaking when they are immersed in a noisy environment, a reflex known as Lombard effect. Current speech enhancement systems based on deep learning do not usually take into account this change in the speaking style, because they are trained with neutral (non-Lombard) speech utterances recorded under quiet conditions to which noise is artificially added. In this paper, we investigate the effects that the Lombard reflex has on the performance of audio-visual speech enhancement systems based on deep learning. The results show that a gap in the performance of as much as approximately 5 dB between the systems trained on neutral speech and the ones trained on Lombard speech exists. This indicates the benefit of taking into account the mismatch between neutral and Lombard speech in the design of audio-visual speech enhancement systems

Phonetic accommodation in non‑native directed speech supports L2 word learning and pronunciation

Published: 02 December 2023This study assessed whether Non-native Directed Speech (NNDS) facilitates second language (L2) learning, specifically L2 word learning and production. Spanish participants (N = 50) learned novel English words, presented either in NNDS or Native-Directed Speech (NDS), in two tasks: Recognition and Production. Recognition involved matching novel objects to their labels produced in NNDS or NDS. Production required participants to pronounce these objects’ labels. The novel words contained English vowel contrasts, which approximated Spanish vowel categories more (/i-ɪ/) or less (/ʌ-æ/). Participants in the NNDS group exhibited faster recognition of novel words, improved learning, and produced the /i-ɪ/ contrast with greater distinctiveness in comparison to the NDS group. Participants’ ability to discriminate the target vowel contrasts was also assessed before and after the tasks, with no improvement detected in the two groups. These findings support the didactic assumption of NNDS, indicating the relevance of the phonetic adaptations in this register for successful L2 acquisition.This research was supported by a Doctoral Fellowship (LCF/BQ/DI19/11730045) from “La Caixa” Foundation (ID 100010434) to G.P., and by the Spanish Ministry of Science and Innovation through the Ramon y Cajal Research Fellowship (RYC2018-024284-I) to M.K. This research was supported by the Basque Government through the BERC 2022-2025 program and by the Spanish State Research Agency through BCBL Severo Ochoa excellence accreditation CEX2020-001010-S. The research was also supported by the Spanish Ministry of Economy and Competitiveness (PID2020-113926GB-I00 to C.D.M.), and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 819093 to C.D.M.)

The impact of the Lombard effect on audio and visual speech recognition systems

When producing speech in noisy backgrounds talkers reflexively adapt their speaking style in ways that increase speech-in-noise intelligibility. This adaptation, known as the Lombard effect, is likely to have an adverse effect on the performance of automatic speech recognition systems that have not been designed to anticipate it. However, previous studies of this impact have used very small amounts of data and recognition systems that lack modern adaptation strategies. This paper aims to rectify this by using a new audio-visual Lombard corpus containing speech from 54 different speakers – significantly larger than any previously available – and modern state-of-the-art speech recognition techniques. The paper is organised as three speech-in-noise recognition studies. The first examines the case in which a system is presented with Lombard speech having been exclusively trained on normal speech. It was found that the Lombard mismatch caused a significant decrease in performance even if the level of the Lombard speech was normalised to match the level of normal speech. However, the size of the mismatch was highly speaker-dependent thus explaining conflicting results presented in previous smaller studies. The second study compares systems trained in matched conditions (i.e., training and testing with the same speaking style). Here the Lombard speech affords a large increase in recognition performance. Part of this is due to the greater energy leading to a reduction in noise masking, but performance improvements persist even after the effect of signal-to-noise level difference is compensated. An analysis across speakers shows that the Lombard speech energy is spectro-temporally distributed in a way that reduces energetic masking, and this reduction in masking is associated with an increase in recognition performance. The final study repeats the first two using a recognition system training on visual speech. In the visual domain, performance differences are not confounded by differences in noise masking. It was found that in matched-conditions Lombard speech supports better recognition performance than normal speech. The benefit was consistently present across all speakers but to a varying degree. Surprisingly, the Lombard benefit was observed to a small degree even when training on mismatched non-Lombard visual speech, i.e., the increased clarity of the Lombard speech outweighed the impact of the mismatch. The paper presents two generally applicable conclusions: i) systems that are designed to operate in noise will benefit from being trained on well-matched Lombard speech data, ii) the results of speech recognition evaluations that employ artificial speech and noise mixing need to be treated with caution: they are overly-optimistic to the extent that they ignore a significant source of mismatch but at the same time overly-pessimistic in that they do not anticipate the potential increased intelligibility of the Lombard speaking style

Methods for speaking style conversion from normal speech to high vocal effort speech

This thesis deals with vocal-effort-focused speaking style conversion (SSC). Specifically, we studied two topics on conversion of normal speech to high vocal effort. The first topic involves the conversion of normal speech to shouted speech. We employed this conversion in a speaker recognition system with vocal effort mismatch between test and enrollment utterances (shouted speech vs. normal speech). The mismatch causes a degradation of the system's speaker identification performance. As solution, we proposed a SSC system that included a novel spectral mapping, used along a statistical mapping technique, to transform the mel-frequency spectral energies of normal speech enrollment utterances towards their counterparts in shouted speech. We evaluated the proposed solution by comparing speaker identification rates for a state-of-the-art i-vector-based speaker recognition system, with and without applying SSC to the enrollment utterances. Our results showed that applying the proposed SSC pre-processing to the enrollment data improves considerably the speaker identification rates. The second topic involves a normal-to-Lombard speech conversion. We proposed a vocoder-based parametric SSC system to perform the conversion. This system first extracts speech features using the vocoder. Next, a mapping technique, robust to data scarcity, maps the features. Finally, the vocoder synthesizes the mapped features into speech. We used two vocoders in the conversion system, for comparison: a glottal vocoder and the widely used STRAIGHT. We assessed the converted speech from the two vocoder cases with two subjective listening tests that measured similarity to Lombard speech and naturalness. The similarity subjective test showed that, for both vocoder cases, our proposed SSC system was able to convert normal speech to Lombard speech. The naturalness subjective test showed that the converted samples using the glottal vocoder were clearly more natural than those obtained with STRAIGHT

Investigating the Lombard Effect Influence on End-to-End Audio-Visual Speech Recognition

Several audio-visual speech recognition models have been recently proposed which aim to improve the robustness over audio-only models in the presence of noise. However, almost all of them ignore the impact of the Lombard effect, i.e., the change in speaking style in noisy environments which aims to make speech more intelligible and affects both the acoustic characteristics of speech and the lip movements. In this paper, we investigate the impact of the Lombard effect in audio-visual speech recognition. To the best of our knowledge, this is the first work which does so using end-to-end deep architectures and presents results on unseen speakers. Our results show that properly modelling Lombard speech is always beneficial. Even if a relatively small amount of Lombard speech is added to the training set then the performance in a real scenario, where noisy Lombard speech is present, can be significantly improved. We also show that the standard approach followed in the literature, where a model is trained and tested on noisy plain speech, provides a correct estimate of the video-only performance and slightly underestimates the audio-visual performance. In case of audio-only approaches, performance is overestimated for SNRs higher than -3dB and underestimated for lower SNRs.Comment: Accepted for publication at Interspeech 201

An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation

Speech enhancement and speech separation are two related tasks, whose purpose is to extract either one or more target speech signals, respectively, from a mixture of sounds generated by several sources. Traditionally, these tasks have been tackled using signal processing and machine learning techniques applied to the available acoustic signals. Since the visual aspect of speech is essentially unaffected by the acoustic environment, visual information from the target speakers, such as lip movements and facial expressions, has also been used for speech enhancement and speech separation systems. In order to efficiently fuse acoustic and visual information, researchers have exploited the flexibility of data-driven approaches, specifically deep learning, achieving strong performance. The ceaseless proposal of a large number of techniques to extract features and fuse multimodal information has highlighted the need for an overview that comprehensively describes and discusses audio-visual speech enhancement and separation based on deep learning. In this paper, we provide a systematic survey of this research topic, focusing on the main elements that characterise the systems in the literature: acoustic features; visual features; deep learning methods; fusion techniques; training targets and objective functions. In addition, we review deep-learning-based methods for speech reconstruction from silent videos and audio-visual sound source separation for non-speech signals, since these methods can be more or less directly applied to audio-visual speech enhancement and separation. Finally, we survey commonly employed audio-visual speech datasets, given their central role in the development of data-driven approaches, and evaluation methods, because they are generally used to compare different systems and determine their performance

Individual and environment-related acoustic-phonetic strategies for communicating in adverse conditions

In many situations it is necessary to produce speech in ‘adverse conditions’: that is, conditions that make speech communication difficult. Research has demonstrated that speaker strategies, as described by a range of acoustic-phonetic measures, can vary both at the individual level and according to the environment, and are argued to facilitate communication. There has been debate as to the environmental specificity of these adaptations, and their effectiveness in overcoming communication difficulty. Furthermore, the manner and extent to which adaptation strategies differ between individuals is not yet well understood. This thesis presents three studies that explore the acoustic-phonetic adaptations of speakers in noisy and degraded communication conditions and their relationship with intelligibility. Study 1 investigated the effects of temporally fluctuating maskers on global acoustic-phonetic measures associated with speech in noise (Lombard speech). The results replicated findings of increased power in the modulation spectrum in Lombard speech, but showed little evidence of adaptation to masker fluctuations via the temporal envelope. Study 2 collected a larger corpus of semi-spontaneous communicative speech in noise and other degradations perturbing specific acoustic dimensions. Speakers showed different adaptations across the environments that were likely suited to overcome noise (steady and temporally fluctuating), restricted spectral and pitch information by a noise-excited vocoder, and a sensorineural hearing loss simulation. Analyses of inter-speaker variation in both studies 1 and 2 showed behaviour was highly variable and some strategy combinations were identified. Study 3 investigated the intelligibility of strategies ‘tailored’ to specific environments and the relationship between intelligibility and speaker acoustics, finding a benefit of tailored speech adaptations and discussing the potential roles of speaker flexibility, adaptation level, and intrinsic intelligibility. The overall results are discussed in relation to models of communication in adverse conditions and a model accounting for individual variability in these conditions is proposed