Effects of oral and oropharyngeal cancer on speech intelligibility using acoustic analysis: Systematic review

Balaguer, Mathieu; Pommée, Timothy; Farinas, Jérôme; Pinquier, Julien; Woisard, Virginie; Speyer, Renée

doi:10.1002/hed.25949

Cited by 22 publications

(12 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, perceptual evaluation has long been the most used method of disordered speech assessment. On the other hand, perceptual evaluation is usually time-consuming, biased and variant, since the evaluation can depend, amongst other facts, on the previous assessments that the health practitioner performed, affecting reproducibility [3]. Hence, the reliability of perceptual evaluations is mostly listener-dependent [4].With the increasing rate of oropharyngeal cancer incidence and the interjudge/intrajudge variance, the development of an automatic assessment that is able to output unbiased intelligibility measures becomes relevant [5,6].…”

Section: Introductionmentioning

confidence: 99%

Automatic Prediction of Speech Intelligibility Based on X-Vectors in the Context of Head and Neck Cancer

Quintas¹,

Mauclair²,

Woisard³

et al. 2020

Interspeech 2020

Self Cite

View full text Add to dashboard Cite

In the context of pathological speech, perceptual evaluation is still the most widely used method for intelligibility estimation. Despite being considered a staple in clinical settings, it has a well-known subjectivity associated with it, which results in greater variances and low reproducibility. On the other hand, due to the increasing computing power and latest research, automatic evaluation has become a growing alternative to perceptual assessments. In this paper we investigate an automatic prediction of speech intelligibility using the x-vector paradigm, in the context of head and neck cancer. Experimental evaluation of the proposed model suggests a high correlation rate when applied to our corpus of HNC patients (p = 0.85). Our approach also displayed the possibility of achieving very high correlation values (p = 0.95) when adapting the evaluation to each individual speaker, displaying a significantly more accurate prediction whilst using smaller amounts of data. These results can also provide valuable insight to the redevelopment of test protocols, which typically tend to be substantial and effort-intensive for patients.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic Prediction of Speech Intelligibility Based on X-Vectors in the Context of Head and Neck Cancer

Quintas¹,

Mauclair²,

Woisard³

et al. 2020

Interspeech 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…As a result, perceptual evaluation has long been the most used method for therapists to assess disordered speech. On the other hand, perceptual evaluations are very time-consuming, biased and variant, since the evaluation can be conditioned on, for example, patients previously assessed by the same therapist [1]. Due to the biased nature and low reproducibility of these scores, and also due to the increasing rate of oropharyngeal cancer incidence, the development of an automatic assessment that is able to output unbiased and reproducible intelligibility measures becomes of high interest [2,3].…”

Section: Introductionmentioning

confidence: 99%

Automatic Assessment of Speech Intelligibility using Consonant Similarity for Head and Neck Cancer

et al. 2022

Self Cite

View full text Add to dashboard Cite

The automatic prediction of speech intelligibility is a widely known problem in the context of pathological speech. It has been seen as a growing and viable alternative to perceptual evaluation, which is typically time-consuming, highly subjective and strongly biased. Due to this, the development of automatic systems that are able to output not only unbiased predictions, but also interpretable scores become relevant. In this paper we investigate a method to predict speech intelligibility based on consonant phonetic similarity. The proposed methodology relies on a siamese network to compute similarity scores between healthy and pathological phonemes, and based on the combination of those scores, regresses the intelligibility values. Our experimental evaluation suggests a high baseline correlation value of p = 0.82, when applied to our corpus of head and neck cancer. Moreover, further conditioning of the system on specific phonemes in key contexts increased the correlation up to p = 0.89. The given methodology also aims to promote interpretability of the predicted intelligibility score, which is highly relevant in a clinical setting.

show abstract

“…Very few tools are available for the evaluation of speech disorders, although it is the most common symptom in cancers of the oral cavity and the pharynx 10 . This evaluation is based on perceptual evaluations, mostly conducted by speech–language pathologists (SLP).…”

Section: Introductionmentioning

confidence: 99%

Construction of an automatic score for the evaluation of speech disorders among patients treated for a cancer of the oral cavity or the oropharynx: The Carcinologic Speech Severity Index

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background Speech disorders impact quality of life for patients treated with oral cavity and oropharynx cancers. However, there is a lack of uniform and applicable methods for measuring the impact on speech production after treatment in this tumor location. Objective The objective of this work is to (1) model an automatic severity index of speech applicable in clinical practice, that is equivalent or superior to a severity score obtained by human listeners, via several acoustics parameters extracted (a) directly from speech signal and (b) resulting from speech processing and (2) derive an automatic speech intelligibility classification (i.e., mild, moderate, severe) to predict speech disability and handicap by combining the listener comprehension score with self‐reported quality of life related to speech. Methods Eighty‐seven patients treated for cancer of the oral cavity or the oropharynx and 35 controls performed different tasks of speech production and completed questionnaires on speech‐related quality of life. The audio recordings were then evaluated by human perception and automatic speech processing. Then, a score was developed through a classic logistic regression model allowing description of the severity of patients' speech disorders. Results Among the group of parameters subject to extraction from automatic processing of the speech signal, six were retained, producing a correlation at 0.87 with the perceptual reference score, 0.77 with the comprehension score, and 0.5 with speech‐related quality of life. The parameters that contributed the most are based on automatic speech recognition systems. These are mainly the automatic average normalized likelihood score on a text reading task and the score of cumulative rankings on pseudowords. The reduced automatic YC2SI is modeled in this way: YC2SIp = 11.48726 + (1.52926 × Xaveraged normalized likelihood reading) + (−1.94e‐06 × Xscore of cumulative ranks pseudowords). Conclusion Automatic processing of speech makes it possible to arrive at valid, reliable, and reproducible parameters able to serve as references in the framework of follow‐up of patients treated for cancer of the oral cavity or the oropharynx.

show abstract

Effects of oral and oropharyngeal cancer on speech intelligibility using acoustic analysis: Systematic review

Cited by 22 publications

References 46 publications

Automatic Prediction of Speech Intelligibility Based on X-Vectors in the Context of Head and Neck Cancer

Automatic Prediction of Speech Intelligibility Based on X-Vectors in the Context of Head and Neck Cancer

Automatic Assessment of Speech Intelligibility using Consonant Similarity for Head and Neck Cancer

Construction of an automatic score for the evaluation of speech disorders among patients treated for a cancer of the oral cavity or the oropharynx: The Carcinologic Speech Severity Index

Contact Info

Product

Resources

About