Enhanced amplitude modulations contribute to the Lombard intelligibility benefit: Evidence from the Nijmegen Corpus of Lombard Speech

Bosker, Hans R.; Cooke, Martin

doi:10.1121/10.0000646

Cited by 11 publications

(5 citation statements)

References 52 publications

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“…As expected, and in line with previous studies [ 3 , 5 , 23 , 57 ], it was confirmed that the lists recorded under the Lombard effect are more intelligible than the lists recorded in silence, maintaining greater discrimination as SNR decreases and greater intelligibility when presented in the same SNR conditions.…”

Section: Discussionsupporting

confidence: 91%

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Design and Development of a Spanish Hearing Test for Speech in Noise (PAHRE)

2022

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Background: There are few hearing tests in Spanish that assess speech discrimination in noise in the adult population that take into account the Lombard effect. This study presents the design and development of a Spanish hearing test for speech in noise (Prueba Auditiva de Habla en Ruido en Español (PAHRE) in Spanish). The pattern of the Quick Speech in Noise test was followed when drafting sentences with five key words each grouped in lists of six sentences. It was necessary to take into account the differences between English and Spanish. Methods: A total of 61 people (24 men and 37 women) with an average age of 46.9 (range 18–84 years) participated in the study. The work was carried out in two phases. In the first phase, a list of Spanish sentences was drafted and subjected to a familiarity test based on the semantic and syntactic characteristics of the sentences; as a result, a list of sentences was selected for the final test. In the second phase, the selected sentences were recorded with and without the Lombard effect, the equivalence between both lists was analysed, and the test was applied to a first reference population. Results: The results obtained allow us to affirm that it is representative of the Spanish spoken in its variety in peninsular Spain. Conclusions: In addition, these results point to the usefulness of the PAHRE test in assessing speech in noise by maintaining a fixed speech intensity while varying the intensity of the multi-speaker background noise. The incorporation of the Lombard effect in the test shows discrimination differences with the same signal-to-noise ratio compared to the test without the Lombard effect.

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Section: Discussionsupporting

confidence: 91%

“…In Junqua [ 16 ], significant differences were also found between male and female speakers, with Lombard speech by females being more intelligible. This set of features makes speech produced under the Lombard effect more intelligible than speech produced in silence when presented under equal SNR conditions [ 3 , 5 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Development of a Spanish Hearing Test for Speech in Noise (PAHRE)

2022

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“…Our proposed TTS speech was shorter than human speech, perhaps due to the speaking rate difference between the speech in WSJ training materials and our natural Lombard speech data. In natural speech, the Lombard effect is not simply a temporal envelope expansion from normal speech and it produce more pronounced amplitude modulations in noise [49], which is not limited to intensity, pitch, and duration enhancement. This might also be the reason for the performance difference between human and TTS speech, since our TTS was trained using Lombard speech with a focus on prosody improvement.…”

Section: B Non-incremental Tts In Static Noise Conditionsmentioning

confidence: 99%

A Machine Speech Chain Approach for Dynamically Adaptive Lombard TTS in Static and Dynamic Noise Environments

Novitasari

Sakti

Nakamura

2022

IEEE/ACM Trans. Audio Speech Lang. Process.

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Recent end-to-end text-to-speech synthesis (TTS) systems have successfully synthesized high-quality speech. However, TTS speech intelligibility degrades in noisy environments because most of these systems were not designed to handle noisy environments. Several works attempted to address this problem by using offline fine-tuning to adapt their TTS to noisy conditions. Unlike machines, humans never perform offline fine-tuning. Instead, they speak with the Lombard effect in noisy places, where they dynamically adjust their vocal effort to improve the audibility of their speech. This ability is supported by the speech chain mechanism, which involves auditory feedback passing from speech perception to speech production. This paper proposes an alternative approach to TTS in noisy environments that is closer to the human Lombard effect. Specifically, we implement Lombard TTS in a machine speech chain framework to synthesize speech with dynamic adaptation. Our TTS performs adaptation by generating speech utterances based on the auditory feedback that consists of the automatic speech recognition (ASR) loss as the speech intelligibility measure and the speech-to-noise ratio (SNR) prediction as power measurement. Two versions of TTS are investigated: non-incremental TTS with utterancelevel feedback and incremental TTS (ITTS) with short-term feedback to reduce the delay without significant performance loss. Furthermore, we evaluate the TTS systems in both static and dynamic noise conditions. Our experimental results show that auditory feedback enhanced the TTS speech intelligibility in noise.

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“…In the original study about the speech-in-noise data set B (Bosker & Cooke, 2020; Experiment 2), the authors reported that speech produced in noise (Lombard speech) was more intelligible in noise-measured in human PWC scores-when compared to speech produced in quiet (plain speech), even when played at the same SNR. This phenomenon, known as the Lombard speech intelligibility benefit in noise, was first discovered by Dreher and O'Neill (1957) and has been replicated in a large body of literature ever since (Chung et al, 2005;Junqua, 1993;Lu & Cooke, 2008; Pittman & Wiley, Summers et al, 1988).…”

Section: Tsr Shows Best Relation To Acoustic Predictors Of Intelligibmentioning

confidence: 99%

Using fuzzy string matching for automated assessment of listener transcripts in speech intelligibility studies

Bosker

2021

Behav Res

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Many studies of speech perception assess the intelligibility of spoken sentence stimuli by means of transcription tasks (‘type out what you hear’). The intelligibility of a given stimulus is then often expressed in terms of percentage of words correctly reported from the target sentence. Yet scoring the participants’ raw responses for words correctly identified from the target sentence is a time-consuming task, and hence resource-intensive. Moreover, there is no consensus among speech scientists about what specific protocol to use for the human scoring, limiting the reliability of human scores. The present paper evaluates various forms of fuzzy string matching between participants’ responses and target sentences, as automated metrics of listener transcript accuracy. We demonstrate that one particular metric, the token sort ratio, is a consistent, highly efficient, and accurate metric for automated assessment of listener transcripts, as evidenced by high correlations with human-generated scores (best correlation: r = 0.940) and a strong relationship to acoustic markers of speech intelligibility. Thus, fuzzy string matching provides a practical tool for assessment of listener transcript accuracy in large-scale speech intelligibility studies. See https://tokensortratio.netlify.app for an online implementation.

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Enhanced amplitude modulations contribute to the Lombard intelligibility benefit: Evidence from the Nijmegen Corpus of Lombard Speech

Cited by 11 publications

References 52 publications

Design and Development of a Spanish Hearing Test for Speech in Noise (PAHRE)

Design and Development of a Spanish Hearing Test for Speech in Noise (PAHRE)

A Machine Speech Chain Approach for Dynamically Adaptive Lombard TTS in Static and Dynamic Noise Environments

Using fuzzy string matching for automated assessment of listener transcripts in speech intelligibility studies

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