Inappropriate Pause Detection in Dysarthric Speech Using Large-Scale Speech Recognition

Core Concepts

The author proposes extending a large-scale speech recognition model for inappropriate pause detection in dysarthric speech, emphasizing the importance of identifying pauses for severity assessment and therapy.

Abstract

The content discusses the detection of inappropriate pauses in dysarthric speech using a large-scale speech recognition model. It highlights the significance of identifying these pauses for assessing severity and guiding speech-language therapy. The proposed method involves treating pause detection as a speech recognition problem, labeling pause locations at the text level, and collaborating with professionals to establish criteria for inappropriate pauses. The experiments show that the approach outperforms baselines in detecting inappropriate pauses.

Stats

Inappropriate Pause Error Rate: 14.47%
WER(%): 25.31%
CER(%): 11.96%
PauER(%): 3.077%

Quotes

"We propose to extend a large-scale speech recognition model for inappropriate pause detection in dysarthric speech."
"Our experiments show that the proposed method better detects inappropriate pauses in dysarthric speech than baselines."

Key Insights Distilled From

Inappropriate Pause Detection In Dysarthric Speech Using Large-Scale Speech Recognition

by Jeehyun Lee,... at arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.18923.pdf

Deeper Inquiries

How can incorporating pause detection into ASR models improve overall performance beyond just detecting pauses

Incorporating pause detection into ASR models can enhance overall performance by not only detecting pauses but also improving speech recognition accuracy. By treating pauses as distinct tokens in the ASR model, the system gains a deeper understanding of speech patterns and structures. This approach allows for more precise transcription by capturing natural breaks in speech flow, which can aid in segmenting sentences and phrases accurately. Additionally, integrating pause detection helps in differentiating between appropriate and inappropriate pauses, providing valuable insights into speech intelligibility and fluency. Overall, this integration leads to a more robust ASR system that produces more accurate transcriptions with improved contextual understanding.

What are potential limitations or biases introduced by automating pause detection and appropriateness assessment

Automating pause detection and appropriateness assessment may introduce potential limitations or biases that need to be carefully addressed. One limitation could stem from the variability in individual speaking styles or dialects that might affect the model's ability to generalize effectively across diverse populations. Biases could arise if the training data predominantly represents certain demographics or accents, leading to disparities in performance when applied to other groups. Moreover, automated systems may struggle with nuanced linguistic cues or cultural contexts that influence how pauses are perceived within speech patterns. It is crucial to continuously evaluate these systems for fairness and accuracy while considering ethical implications related to bias mitigation strategies.

How might advancements in ASR technology impact the accuracy and efficiency of detecting inappropriate pauses

Advancements in ASR technology have the potential to significantly impact the accuracy and efficiency of detecting inappropriate pauses in several ways:

Improved Recognition Accuracy: Advanced ASR models with enhanced acoustic modeling capabilities can better capture subtle variations in speech signals associated with dysarthric conditions, leading to more accurate transcription results.

Enhanced Contextual Understanding: State-of-the-art ASR technologies incorporating deep learning techniques can analyze larger context windows during transcription, enabling better comprehension of sentence structures and semantic relationships that contribute to identifying inappropriate pauses.

Efficient Training Processes: With advancements like self-supervised learning frameworks such as wav2vec 2.0 mentioned above, training ASR models becomes more efficient due to leveraging large-scale weak supervision methods for robust recognition tasks.

Real-time Feedback Mechanisms: As real-time processing capabilities improve within ASR systems, there is potential for immediate feedback on detected inappropriate pauses during live interactions or therapy sessions without significant delays.

5 .Cross-linguistic Adaptability: Advancements allow for easier adaptation of models across languages by leveraging transfer learning techniques or multilingual approaches—enabling broader applicability beyond specific language datasets used during training processes.
These advancements collectively contribute towards enhancing both the accuracy and efficiency of detecting inappropriate pauses within dysarthric speech using advanced ASR technologies."