An adaptive post-filtering method producing an artificial Lombard-like effect for intelligibility enhancement of narrowband telephone speech

Jokinen, Emma; Takanen, Marko; Vainio, Martti; Alku, Paavo

doi:10.1016/j.csl.2013.03.005

Cited by 20 publications

(9 citation statements)

References 12 publications

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“…Also, speech that is deliberately spoken clearly is easier to understand than conversational speech for both normal and hearing-impaired listeners (Picheny et al, 1985;Payton et al, 1994;Ferguson and Kewley-Port, 2002) and for non-native listeners (Bradlow and Bent, 2002;Cooke and Lecumberri, 2012). Therefore, one approach is to process conversational speech so as to mimic the effects that occur in "Lombard" speech or clearly spoken speech (Skowronski and Harris, 2006;Zorilȃ et al, 2012;Takou et al, 2013;Godoy et al, 2014;Jokinen et al, 2014;Erro et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of near-end speech enhancement under equal-loudness constraint for listeners with normal-hearing and mild-to-moderate hearing loss

Zorilă

Stylianou

Flanagan

et al. 2017

The Journal of the Acoustical Society of America

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Four algorithms designed to enhance the intelligibility of speech when noise is added after processing were evaluated under the constraint that the speech should have the same loudness before and after processing, as determined using a loudness model. The algorithms applied spectral modifications and two of them included dynamic-range compression. On average, the methods with dynamic-range compression required the least level adjustment to equate loudness for the unprocessed and processed speech. Subjects with normal-hearing (experiment 1) and mild-to-moderate hearing loss (experiment 2) were tested using unmodified and enhanced speech presented in speech-shaped noise (SSN) and a competing speaker (CS). The results showed (a) the algorithms with dynamic-range compression yielded the largest intelligibility gains in both experiments and for both types of background; (b) the algorithms without dynamic-range compression either yielded benefit only with the SSN or yielded no consistent benefit; (c) speech reception thresholds for unprocessed speech were higher for hearing-impaired than for normal-hearing subjects, by about 2 dB for the SSN and 6 dB for the CS. It is concluded that the enhancement methods incorporating dynamic-range compression can improve intelligibility under the equal-loudness constraint for both normal-hearing and hearing-impaired subjects and for both steady and fluctuating backgrounds.

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Section: Introductionmentioning

confidence: 99%

Evaluation of near-end speech enhancement under equal-loudness constraint for listeners with normal-hearing and mild-to-moderate hearing loss

Zorilă

Stylianou

Flanagan

et al. 2017

The Journal of the Acoustical Society of America

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“…Most of both research and application areas are, however, related to human (and human-computer) communication, telecommunications, etc. [9], [19], [20]. Especially important are strategies for improving speech comprehensibility in noisy conditions based on various techniques, including speech modeling.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Lombard Speech Models in the Context of Speech in Noise Enhancement

et al. 2020

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The Lombard effect is one of the most well-known effects of noise on speech production. Speech with the Lombard effect is more easily recognizable in noisy environments than normal natural speech. Our previous investigations showed that speech synthesis models might retain Lombard-effect characteristics. In this study, we investigate several speech models, such as harmonic, source-filter, and sinusoidal, applied to Lombard speech in the context of speech enhancement. For this purpose, 100 utterances of natural speech, and 100 with the Lombard effect induced are used. The goal of this study is to check to what extent speech utterances based on these models are recognizable and at what SNR (Signal-to-Noise Ratio) level threshold a particular model stops working. For this purpose, the synthesized models and Lombard speech are mixed with babble speech and street noise recordings with different SNRs. The quality of these models is measured, employing objective indicators as well as subjective tests. Since there is no standardized measure to apply to enhanced speech, an objective measure of assessing the speech quality of a model synthesizing Lombard speech characteristics, based on a feature vector, is proposed. Our approach is then compared with the standardized metric used in telecommunications as well as with subjective test results. The experimental investigations show the superiority of the source-filter models applied to synthesize Lombard speech over other models utilized. Also, the measure proposed correlates more closely with the results of the subjective evaluation than the outcomes from the ITU-T P.563 recommendation. This was checked with a ANOVA statistical analysis.

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“…There are two major types of NLE systems. Some are applying modifications inspired from previous intelligibility studies (e.g., Lombard speech) [2][3][4][5][6][7], and others are enhancing the speech using transformations that would optimize some objective intelligibility measures well correlated with human perception [8,9]. A method based on spectral shaping and dynamic range compression yielded the state-of-the-art performance at the Hurricane Challenge [1]; there, the participants were asked to boost the intelligibility of clean speech in noise under the constraint of equal root mean square (RMS) before and after modifications.…”

Section: Introductionmentioning

confidence: 99%

On the Quality and Intelligibility of Noisy Speech Processed for Near-End Listening Enhancement

Zorilă¹,

Stylianou²

2017

Interspeech 2017

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Most current techniques for near-end speech intelligibility enhancement have focused on processing clean input signals, however, in realistic environments, the input is often noisy. Processing noisy speech for intelligibility enhancement using algorithms developed for clean signals can lower the perceptual quality of the samples when they are listened in quiet. Here we address the quality loss in these conditions by combining noise reduction with a multi-band version of a state-of-the-art intelligibility enhancer for clean speech that is based on spectral shaping and dynamic range compression (SSDRC). Subjective quality and intelligibility assessments with noisy input speech showed that: (a) In quiet near-end conditions, the proposed system outperformed the baseline SSDRC in terms of Mean Opinion Score (MOS); (b) In speech-shaped near-end noise, the proposed system improved the intelligibility of unprocessed speech by a factor larger than three at the lowest tested signalto-noise ratio (SNR) however, overall, it yielded lower recognition scores than the standard SSDRC.

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An adaptive post-filtering method producing an artificial Lombard-like effect for intelligibility enhancement of narrowband telephone speech

Cited by 20 publications

References 12 publications

Evaluation of near-end speech enhancement under equal-loudness constraint for listeners with normal-hearing and mild-to-moderate hearing loss

Evaluation of near-end speech enhancement under equal-loudness constraint for listeners with normal-hearing and mild-to-moderate hearing loss

Evaluation of Lombard Speech Models in the Context of Speech in Noise Enhancement

On the Quality and Intelligibility of Noisy Speech Processed for Near-End Listening Enhancement

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