Given an orthographic transcription, forced alignment systems automatically determine boundaries between segments in speech, facilitating the use of large corpora. In the present paper, we introduce a neural network-based forced alignment system, the Mason-Alberta Phonetic Segmenter (MAPS). MAPS serves as a testbed for two possible improvements we pursue for forced alignment systems. The first is treating the acoustic model as a tagger, rather than a classifier, motivated by the common understanding that segments are not truly discrete and often overlap. The second is an interpolation technique to allow more precise boundaries than the typical 10 ms limit in modern systems. During testing, all system configurations we trained significantly outperformed the state-of-the-art Montreal Forced Aligner in the 10 ms boundary placement tolerance threshold. The greatest difference achieved was a 28.13 % relative performance increase. The Montreal Forced Aligner began to slightly outperform our models at around a 30 ms tolerance. We also reflect on the training process for acoustic modeling in forced alignment, highlighting how the output targets for these models do not match phoneticians\' conception of similarity between phones and that reconciling this tension may require rethinking the task and output targets or how speech itself should be segmented.

Purpose Cutting-edge automatic speech recognition (ASR) technology holds significant promise in transcribing and recognizing medical information during patient encounters, thereby enabling automatic and real-time clinical documentation, which could significantly alleviate care clinicians\' burdens. Nevertheless, the performance of current-generation ASR technology in analyzing conversations in noisy and dynamic medical settings, such as prehospital or Emergency Medical Services (EMS), lacks sufficient validation. This study explores the current technological limitations and future potential of deploying ASR technology for clinical documentation in fast-paced and noisy medical settings such as EMS. Methods In this study, we evaluated four ASR engines, including Google Speech-to-Text Clinical Conversation, OpenAI Speech-to-Text, Amazon Transcribe Medical, and Azure Speech-to-Text engine. The empirical data used for evaluation were 40 EMS simulation recordings. The transcribed texts were analyzed for accuracy against 23 Electronic Health Records (EHR) categories of EMS. The common types of errors in transcription were also analyzed. Results Among all four ASR engines, Google Speech-to-Text Clinical Conversation performed the best. Among all EHR categories, better performance was observed in categories \"mental state\" (F1 = 1.0), \"allergies\" (F1 = 0.917), \"past medical history\" (F1 = 0.804), \"electrolytes\" (F1 = 1.0), and \"blood glucose level\" (F1 = 0.813). However, all four ASR engines demonstrated low performance in transcribing certain critical categories, such as \"treatment\" (F1 = 0.650) and \"medication\" (F1 = 0.577). Conclusion Current ASR solutions fall short in fully automating the clinical documentation in EMS setting. Our findings highlight the need for further improvement and development of automated clinical documentation technology to improve recognition accuracy in time-critical and dynamic medical settings.

UNASSIGNED: Speech analysis has been expected to help as a screening tool for early detection of Alzheimer\'s disease (AD) and mild-cognitively impairment (MCI). Acoustic features and linguistic features are usually used in speech analysis. However, no studies have yet determined which type of features provides better screening effectiveness, especially in the large aging population of China.
UNASSIGNED: Firstly, to compare the screening effectiveness of acoustic features, linguistic features, and their combination using the same dataset. Secondly, to develop Chinese automated diagnosis model using self-collected natural discourse data obtained from native Chinese speakers.
UNASSIGNED: A total of 92 participants from communities in Shanghai, completed MoCA-B and a picture description task based on the Cookie Theft under the guidance of trained operators, and were divided into three groups including AD, MCI, and heathy control (HC) based on their MoCA-B score. Acoustic features (Pitches, Jitter, Shimmer, MFCCs, Formants) and linguistic features (part-of-speech, type-token ratio, information words, information units) are extracted. The machine algorithms used in this study included logistic regression, random forest (RF), support vector machines (SVM), Gaussian Naive Bayesian (GNB), and k-Nearest neighbor (kNN). The validation accuracies of the same ML model using acoustic features, linguistic features, and their combination were compared.
UNASSIGNED: The accuracy with linguistic features is generally higher than acoustic features in training. The highest accuracy to differentiate HC and AD is 80.77% achieved by SVM, based on all the features extracted from the speech data, while the highest accuracy to differentiate HC and AD or MCI is 80.43% achieved by RF, based only on linguistic features.
UNASSIGNED: Our results suggest the utility and validity of linguistic features in the automated diagnosis of cognitive impairment, and validated the applicability of automated diagnosis for Chinese language data.

This study presents a model of automatic speech recognition (ASR) that is designed to diagnose pronunciation issues in children with speech sound disorders (SSDs) to replace manual transcriptions in clinical procedures. Because ASR models trained for general purposes mainly predict input speech into standard spelling words, well-known high-performance ASR models are not suitable for evaluating pronunciation in children with SSDs. We fine-tuned the wav2vec2.0 XLS-R model to recognise words as they are pronounced by children, rather than converting the speech into their standard spelling words. The model was fine-tuned with a speech dataset of 137 children with SSDs pronouncing 73 Korean words that are selected for actual clinical diagnosis. The model\'s Phoneme Error Rate (PER) was only 10% when its predictions of children\'s pronunciations were compared to human annotations of pronunciations as heard. In contrast, despite its robust performance on general tasks, the state-of-the-art ASR model Whisper showed limitations in recognising the speech of children with SSDs, with a PER of approximately 50%. While the model still requires improvement in terms of the recognition of unclear pronunciation, this study demonstrates that ASR models can streamline complex pronunciation error diagnostic procedures in clinical fields.

In air traffic control (ATC), speech communication with radio transmission is the primary way to exchange information between the controller and the pilot. As a result, the integration of automatic speech recognition (ASR) systems holds immense potential for reducing controllers\' workload and plays a crucial role in various ATC scenarios, which is particularly significant for ATC research. This article provides a comprehensive review of ASR technology\'s applications in the ATC communication system. Firstly, it offers a comprehensive overview of current research, including ATC corpora, ASR models, evaluation measures and application scenarios. A more comprehensive and accurate evaluation methodology tailored for ATC is proposed, considering advancements in communication sensing systems and deep learning techniques. This methodology helps researchers in enhancing ASR systems and improving the overall performance of ATC systems. Finally, future research recommendations are identified based on the primary challenges and issues. The authors sincerely hope this work will serve as a clear technical roadmap for ASR endeavors within the ATC domain and make a valuable contribution to the research community.

The Web has become an essential resource but is not yet accessible to everyone. Assistive technologies and innovative, intelligent frameworks, for example, those using conversational AI, help overcome some exclusions. However, some users still experience barriers. This paper shows how a human-centered approach can shed light on technology limitations and gaps. It reports on a three-step process (focus group, co-design, and preliminary validation) that we adopted to investigate how people with speech impairments, e.g., dysarthria, browse the Web and how barriers can be reduced. The methodology helped us identify challenges and create new solutions, i.e., patterns for Web browsing, by combining voice-based conversational AI, customized for impaired speech, with techniques for the visual augmentation of web pages. While current trends in AI research focus on more and more powerful large models, participants remarked how current conversational systems do not meet their needs, and how it is important to consider each one\'s specificity for a technology to be called inclusive.

UNASSIGNED: To compare verbal fluency scores derived from manual transcriptions to those obtained using automatic speech recognition enhanced with machine learning classifiers.
UNASSIGNED: Using Amazon Web Services, we automatically transcribed verbal fluency recordings from 1400 individuals who performed both animal and letter F verbal fluency tasks. We manually adjusted timings and contents of the automatic transcriptions to obtain \"gold standard\" transcriptions. To make automatic scoring possible, we trained machine learning classifiers to discern between valid and invalid utterances. We then calculated and compared verbal fluency scores from the manual and automatic transcriptions.
UNASSIGNED: For both animal and letter fluency tasks, we achieved good separation of valid versus invalid utterances. Verbal fluency scores calculated based on automatic transcriptions showed high correlation with those calculated after manual correction.
UNASSIGNED: Many techniques for scoring verbal fluency word lists require accurate transcriptions with word timings. We show that machine learning methods can be applied to improve off-the-shelf ASR for this purpose. These automatically derived scores may be satisfactory for some applications. Low correlations among some of the scores indicate the need for improvement in automatic speech recognition before a fully automatic approach can be reliably implemented.

UNASSIGNED: In today\'s world, numerous applications integral to various facets of daily life include automatic speech recognition methods. Thus, the development of a successful automatic speech recognition system can significantly augment the convenience of people\'s daily routines. While many automatic speech recognition systems have been established for widely spoken languages like English, there has been insufficient progress in developing such systems for less common languages such as Turkish. Moreover, due to its agglutinative structure, designing a speech recognition system for Turkish presents greater challenges compared to other language groups. Therefore, our study focused on proposing deep learning models for automatic speech recognition in Turkish, complemented by the integration of a language model.
UNASSIGNED: In our study, deep learning models were formulated by incorporating convolutional neural networks, gated recurrent units, long short-term memories, and transformer layers. The Zemberek library was employed to craft the language model to improve system performance. Furthermore, the Bayesian optimization method was applied to fine-tune the hyper-parameters of the deep learning models. To evaluate the model\'s performance, standard metrics widely used in automatic speech recognition systems, specifically word error rate and character error rate scores, were employed.
UNASSIGNED: Upon reviewing the experimental results, it becomes evident that when optimal hyper-parameters are applied to models developed with various layers, the scores are as follows: Without the use of a language model, the Turkish Microphone Speech Corpus dataset yields scores of 22.2 -word error rate and 14.05-character error rate, while the Turkish Speech Corpus dataset results in scores of 11.5 -word error rate and 4.15 character error rate. Upon incorporating the language model, notable improvements were observed. Specifically, for the Turkish Microphone Speech Corpus dataset, the word error rate score decreased to 9.85, and the character error rate score lowered to 5.35. Similarly, the word error rate score improved to 8.4, and the character error rate score decreased to 2.7 for the Turkish Speech Corpus dataset. These results demonstrate that our model outperforms the studies found in the existing literature.

This research presents the development of a cutting-edge real-time multilingual speech recognition and speaker diarization system that leverages OpenAI\'s Whisper model. The system specifically addresses the challenges of automatic speech recognition (ASR) and speaker diarization (SD) in dynamic, multispeaker environments, with a focus on accurately processing Mandarin speech with Taiwanese accents and managing frequent speaker switches. Traditional speech recognition systems often fall short in such complex multilingual and multispeaker contexts, particularly in SD. This study, therefore, integrates advanced speech recognition with speaker diarization techniques optimized for real-time applications. These optimizations include handling model outputs efficiently and incorporating speaker embedding technology. The system was evaluated using data from Taiwanese talk shows and political commentary programs, featuring 46 diverse speakers. The results showed a promising word diarization error rate (WDER) of 2.68% in two-speaker scenarios and 11.65% in three-speaker scenarios, with an overall WDER of 6.96%. This performance is comparable to that of non-real-time baseline models, highlighting the system\'s ability to adapt to various complex conversational dynamics, a significant advancement in the field of real-time multilingual speech processing.

BACKGROUND: Prior research has associated spoken language use with depression, yet studies often involve small or non-clinical samples and face challenges in the manual transcription of speech. This paper aimed to automatically identify depression-related topics in speech recordings collected from clinical samples.
METHODS: The data included 3919 English free-response speech recordings collected via smartphones from 265 participants with a depression history. We transcribed speech recordings via automatic speech recognition (Whisper tool, OpenAI) and identified principal topics from transcriptions using a deep learning topic model (BERTopic). To identify depression risk topics and understand the context, we compared participants\' depression severity and behavioral (extracted from wearable devices) and linguistic (extracted from transcribed texts) characteristics across identified topics.
RESULTS: From the 29 topics identified, we identified 6 risk topics for depression: \'No Expectations\', \'Sleep\', \'Mental Therapy\', \'Haircut\', \'Studying\', and \'Coursework\'. Participants mentioning depression risk topics exhibited higher sleep variability, later sleep onset, and fewer daily steps and used fewer words, more negative language, and fewer leisure-related words in their speech recordings.
CONCLUSIONS: Our findings were derived from a depressed cohort with a specific speech task, potentially limiting the generalizability to non-clinical populations or other speech tasks. Additionally, some topics had small sample sizes, necessitating further validation in larger datasets.
CONCLUSIONS: This study demonstrates that specific speech topics can indicate depression severity. The employed data-driven workflow provides a practical approach for analyzing large-scale speech data collected from real-world settings.