Tracheostomy cannulas and voice prosthesis

Chaos Behavior Analysis of Alaryngeal Voices Including Esophageal and Tracheoesophageal Voices

HYPOTHESIS/OBJECTIVES

This study's objective was to develop a method to evaluate the chaotic characteristic of alaryngeal speech. The proposed method will be capable of distinguishing between normal and alaryngeal voices, including esophageal (SE) and tracheoesophageal (TE) voices. It has been previously shown that alaryngeal voices exhibit chaotic characteristics due to the aperiodicity of their signals. The proposed method will be applied for future use to quantify both chaos behavior (CB) and the difference between SE and TE voices.

STUDY DESIGN

A total of 74 voice recordings including 34 normal and 40 alaryngeal (26 SE and 14 TE) were used in the study. Voice samples were analyzed to distinguish alaryngeal voices from normal voices and to investigate different chaotic characteristics of SE and TE speech.

METHODS

A chaotic distribution detection-based method was used to investigate the CB of alaryngeal voices. This CB was used to detect the difference between SE and TE voice types. Quantification of the CB parameter was performed. Statistical analyses were used to compare the results of the CB analysis for both the SE and TE voices.

RESULTS

Statistical analysis revealed that CB effectively differentiated between all normal and alaryngeal voice types (p < 0.01). Subsequent multiclass receiver operating characteristic (ROC) analysis demonstrated that CB (area under the curve) possessed the greatest classification accuracy relative to correlation dimension (D2).

CONCLUSIONS

The CB metric shows strong promise as an accurate, useful metric for objective differentiation between all normal and alaryngaeal, SE and TE voice types. The CB calculations showed expected results, as SE voices have significantly more CB than TE voices, constituting substantial improvement over previous methods and becoming the first SE and TE classification method. This metric can help clinicians obtain additional acoustic information when monitoring the efficacy of treatment for patients undergoing total laryngectomies.

Surface Electromyography-Based Recognition, Synthesis, and Perception of Prosodic Subvocal Speech

Purpose This study aimed to evaluate a novel communication system designed to translate surface electromyographic (sEMG) signals from articulatory muscles into speech using a personalized, digital voice. The system was evaluated for word recognition, prosodic classification, and listener perception of synthesized speech. Method sEMG signals were recorded from the face and neck as speakers with (n = 4) and without (n = 4) laryngectomy subvocally recited (silently mouthed) a speech corpus comprising 750 phrases (150 phrases with variable phrase-level stress). Corpus tokens were then translated into speech via personalized voice synthesis (n = 8 synthetic voices) and compared against phrases produced by each speaker when using their typical mode of communication (n = 4 natural voices, n = 4 electrolaryngeal [EL] voices). Naïve listeners (n = 12) evaluated synthetic, natural, and EL speech for acceptability and intelligibility in a visual sort-and-rate task, as well as phrasal stress discriminability via a classification mechanism. Results Recorded sEMG signals were processed to translate sEMG muscle activity into lexical content and categorize variations in phrase-level stress, achieving a mean accuracy of 96.3% (SD = 3.10%) and 91.2% (SD = 4.46%), respectively. Synthetic speech was significantly higher in acceptability and intelligibility than EL speech, also leading to greater phrasal stress classification accuracy, whereas natural speech was rated as the most acceptable and intelligible, with the greatest phrasal stress classification accuracy. Conclusion This proof-of-concept study establishes the feasibility of using subvocal sEMG-based alternative communication not only for lexical recognition but also for prosodic communication in healthy individuals, as well as those living with vocal impairments and residual articulatory function. Supplemental Material https://doi.org/10.23641/asha.14558481.

A rare case of acquired benign tracheoesophageal fistula

Acquired benign tracheoesophageal fistula (TOF) is a rare medical condition that usually results from trauma, foreign bodies or granulomatous infections. This is an unusual presentation of a male patient with a history of laryngectomy who has had over a period of several years inappropriately and vigorously used valve cleaning brushes to clean tracheal secretions, which has led to the formation of a TOF. Due to the patient’s obsessive habit, we could not manage him using conventional surgical methods. Instead, we opted for the placement of a salivary bypass tube, which yielded good results and recovery. To the best of our knowledge, no other case of similar aetiology has been published. We would like to highlight the importance of appropriate patient selection and education prior to performing a tracheoesophageal puncture to avoid developing life-threatening complications as demonstrated in our case report.

Silent Speech Recognition as an Alternative Communication Device for Persons with Laryngectomy

Each year thousands of individuals require surgical removal of their larynx (voice box) due to trauma or disease, and thereby require an alternative voice source or assistive device to verbally communicate. Although natural voice is lost after laryngectomy, most muscles controlling speech articulation remain intact. Surface electromyographic (sEMG) activity of speech musculature can be recorded from the neck and face, and used for automatic speech recognition to provide speech-to-text or synthesized speech as an alternative means of communication. This is true even when speech is mouthed or spoken in a silent (subvocal) manner, making it an appropriate communication platform after laryngectomy. In this study, 8 individuals at least 6 months after total laryngectomy were recorded using 8 sEMG sensors on their face (4) and neck (4) while reading phrases constructed from a 2,500-word vocabulary. A unique set of phrases were used for training phoneme-based recognition models for each of the 39 commonly used phonemes in English, and the remaining phrases were used for testing word recognition of the models based on phoneme identification from running speech. Word error rates were on average 10.3% for the full 8-sensor set (averaging 9.5% for the top 4 participants), and 13.6% when reducing the sensor set to 4 locations per individual (n=7). This study provides a compelling proof-of-concept for sEMG-based alaryngeal speech recognition, with the strong potential to further improve recognition performance.