Rhythm is known to play an important role in infant language acquisition, but few infant language development studies have considered that rhythm is multimodal and shows strong connections between speech and the body. Based on the observation that infants sometimes show rhythmic motor responses when listening to auditory rhythms, the present study asked whether specific rhythm cues (pitch, intensity, or duration) would systematically increase infants\' spontaneous rhythmic body movement, and whether their rhythmic movements would be associated with their speech processing abilities. We used pre-existing experimental and video data of 148 German-learning 7.5- and 9.5-month-old infants tested on their use of rhythm as a cue for speech segmentation. The infants were familiarized with an artificial language featuring syllables alternating in pitch, intensity, duration, or none of these cues. Subsequently, they were tested on their recognition of bisyllables based on perceived rhythm. We annotated infants\' rhythmic movements in the videos, analyzed whether the rhythmic moving durations depended on the perceived rhythmic cue, and correlated them with the speech segmentation performance. The result was that infants\' motor engagement was highest when they heard a duration-based speech rhythm. Moreover, we found an association of the quantity of infants\' rhythmic motor responses and speech segmentation. However, contrary to the predictions, infants who exhibited fewer rhythmic movements showed a more mature performance in speech segmentation. In sum, the present study provides initial exploratory evidence that infants\' spontaneous rhythmic body movements while listening to rhythmic speech are systematic, and may be linked with their language processing. Moreover, the results highlight the need for considering infants\' spontaneous rhythmic body movements as a source of individual differences in infant auditory and speech perception.

Infants begin to segment word forms from fluent speech-a crucial task in lexical processing-between 4 and 7 months of age. Prior work has established that infants rely on a variety of cues available in the speech signal (i.e., prosodic, statistical, acoustic-segmental, and lexical) to accomplish this task. In two experiments with French-learning 6- and 10-month-olds, we use a psychoacoustic approach to examine if and how degradation of the two fundamental acoustic components extracted from speech by the auditory system, namely, temporal (both frequency and amplitude modulation) and spectral information, impact word form segmentation. Infants were familiarized with passages containing target words, in which frequency modulation (FM) information was replaced with pure tones using a vocoder, while amplitude modulation (AM) was preserved in either 8 or 16 spectral bands. Infants were then tested on their recognition of the target versus novel control words. While the 6-month-olds were unable to segment in either condition, the 10-month-olds succeeded, although only in the 16 spectral band condition. These findings suggest that 6-month-olds need FM temporal cues for speech segmentation while 10-month-olds do not, although they need the AM cues to be presented in enough spectral bands (i.e., 16). This developmental change observed in infants\' sensitivity to spectrotemporal cues likely results from an increase in the range of available segmentation procedures, and/or shift from a vowel to a consonant bias in lexical processing between the two ages, as vowels are more affected by our acoustic manipulations. RESEARCH HIGHLIGHTS: Although segmenting speech into word forms is crucial for lexical acquisition, the acoustic information that infants\' auditory system extracts to process continuous speech remains unknown. We examined infants\' sensitivity to spectrotemporal cues in speech segmentation using vocoded speech, and revealed a developmental change between 6 and 10 months of age. We showed that FM information, that is, the fast temporal modulations of speech, is necessary for 6- but not 10-month-old infants to segment word forms. Moreover, reducing the number of spectral bands impacts 10-month-olds\' segmentation abilities, who succeed when 16 bands are preserved, but fail with 8 bands.

Acoustic features extracted from speech can help with the diagnosis of neurological diseases and monitoring of symptoms over time. Temporal segmentation of audio signals into individual words is an important pre-processing step needed prior to extracting acoustic features. Machine learning techniques could be used to automate speech segmentation via automatic speech recognition (ASR) and sequence to sequence alignment. While state-of-the-art ASR models achieve good performance on healthy speech, their performance significantly drops when evaluated on dysarthric speech. Fine-tuning ASR models on impaired speech can improve performance in dysarthric individuals, but it requires representative clinical data, which is difficult to collect and may raise privacy concerns. This study explores the feasibility of using two augmentation methods to increase ASR performance on dysarthric speech: 1) healthy individuals varying their speaking rate and loudness (as is often used in assessments of pathological speech); 2) synthetic speech with variations in speaking rate and accent (to ensure more diverse vocal representations and fairness). Experimental evaluations showed that fine-tuning a pre-trained ASR model with data from these two sources outperformed a model fine-tuned only on real clinical data and matched the performance of a model fine-tuned on the combination of real clinical data and synthetic speech. When evaluated on held-out acoustic data from 24 individuals with various neurological diseases, the best performing model achieved an average word error rate of 5.7% and a mean correct count accuracy of 94.4%. In segmenting the data into individual words, a mean intersection-over-union of 89.2% was obtained against manual parsing (ground truth). It can be concluded that emulated and synthetic augmentations can significantly reduce the need for real clinical data of dysarthric speech when fine-tuning ASR models and, in turn, for speech segmentation.

Computational models of infant word-finding typically operate over transcriptions of infant-directed speech corpora. It is now possible to test models of word segmentation on speech materials, rather than transcriptions of speech. We propose that such modeling efforts be conducted over the speech of the experimental stimuli used in studies measuring infants\' capacity for learning from spoken sentences. Correspondence with infant outcomes in such experiments is an appropriate benchmark for models of infants. We demonstrate such an analysis by applying the DP-Parser model of Algayres and colleagues to auditory stimuli used in infant psycholinguistic experiments by Pelucchi and colleagues. The DP-Parser model takes speech as input, and creates multiple overlapping embeddings from each utterance. Prospective words are identified as clusters of similar embedded segments. This allows segmentation of each utterance into possible words, using a dynamic programming method that maximizes the frequency of constituent segments. We show that DP-Parse mimics American English learners\' performance in extracting words from Italian sentences, favoring the segmentation of words with high syllabic transitional probability. This kind of computational analysis over actual stimuli from infant experiments may be helpful in tuning future models to match human performance.

Language learners track conditional probabilities to find words in continuous speech and to map words and objects across ambiguous contexts. It remains unclear, however, whether learners can leverage the structure of the linguistic input to do both tasks at the same time. To explore this question, we combined speech segmentation and cross-situational word learning into a single task. In Experiment 1, when adults (N = 60) simultaneously segmented continuous speech and mapped the newly segmented words to objects, they demonstrated better performance than when either task was performed alone. However, when the speech stream had conflicting statistics, participants were able to correctly map words to objects, but were at chance level on speech segmentation. In Experiment 2, we used a more sensitive speech segmentation measure to find that adults (N = 35), exposed to the same conflicting speech stream, correctly identified non-words as such, but were still unable to discriminate between words and part-words. Again, mapping was above chance. Our study suggests that learners can track multiple sources of statistical information to find and map words to objects in noisy environments. It also prompts questions on how to effectively measure the knowledge arising from these learning experiences.

During speech processing, neural activity in non-autistic adults and infants tracks the speech envelope. Recent research in adults indicates that this neural tracking relates to linguistic knowledge and may be reduced in autism. Such reduced tracking, if present already in infancy, could impede language development. In the current study, we focused on children with a family history of autism, who often show a delay in first language acquisition. We investigated whether differences in tracking of sung nursery rhymes during infancy relate to language development and autism symptoms in childhood. We assessed speech-brain coherence at either 10 or 14 months of age in a total of 22 infants with high likelihood of autism due to family history and 19 infants without family history of autism. We analyzed the relationship between speech-brain coherence in these infants and their vocabulary at 24 months as well as autism symptoms at 36 months. Our results showed significant speech-brain coherence in the 10- and 14-month-old infants. We found no evidence for a relationship between speech-brain coherence and later autism symptoms. Importantly, speech-brain coherence in the stressed syllable rate (1-3 Hz) predicted later vocabulary. Follow-up analyses showed evidence for a relationship between tracking and vocabulary only in 10-month-olds but not in 14-month-olds and indicated possible differences between the likelihood groups. Thus, early tracking of sung nursery rhymes is related to language development in childhood.

The prominent role of allophonic cues in English speech segmentation has widely been recognized by phonologists and psycholinguists. However, very meager inquiry was devoted to analysing the perception of these noncontrastive allophonic cues by Arab EFL learners. Accordingly, the present study is an attempt to examine the exploitation of allophonic cues, mainly aspiration, glottalization and approximant devoicing to English word junctures by 40 Jordanian PhD students. Moreover, it aims to find out which allophonic cues are perceived more accurately during the segmentation process and if there is any evidence for Universal Grammar markedness. The experiment is led through a forced-choice identification task adopted from Altenberg (Second Lang Res 21:325-358, 2005) and Rojczyk et al. (Res Lang 1:15-29, 2016). The results of ANOVA unveiled that there is a statistically significant difference between the three types of allophonic cues, viz. aspiration, glottalization and approximant devoicing. This implies that the participants outperformed in stimuli marked by glottalization than by aspiration and approximant devoicing. This result provided further evidence for the universality of glottalization as a boundary cue in English speech segmentation. Overall, the Jordanian PhD students failed in perceiving the allophonic cues accurately and exploiting them to detect word boundaries. The present inquiry has the potential to provide several recommendations for syllabus designers, and second/foreign language teachers and learners.

Adults are able to use visual prosodic cues in the speaker\'s face to segment speech. Furthermore, eye-tracking data suggest that learners will shift their gaze to the mouth during visual speech segmentation. Although these findings suggest that the mouth may be viewed more than the eyes or nose during visual speech segmentation, no study has examined the direct functional importance of individual features; thus, it is unclear which visual prosodic cues are important for word segmentation. In this study, we examined the impact of first removing (Experiment 1) and then isolating (Experiment 2) individual facial features on visual speech segmentation. Segmentation performance was above chance in all conditions except for when the visual display was restricted to the eye region (eyes only condition in Experiment 2). This suggests that participants were able to segment speech when they could visually access the mouth but not when the mouth was completely removed from the visual display, providing evidence that visual prosodic cues conveyed by the mouth are sufficient and likely necessary for visual speech segmentation.

Parkinson\'s disease (PD) is a neurodegenerative disorder with slow progression whose symptoms can be identified at late stages. Early diagnosis and treatment of PD can help to relieve the symptoms and delay progression. However, this is very challenging due to the similarities between the symptoms of PD and other diseases. The current study proposes a generic framework for the diagnosis of PD using handwritten images and (or) speech signals. For the handwriting images, 8 pre-trained convolutional neural networks (CNN) via transfer learning tuned by Aquila Optimizer were trained on the NewHandPD dataset to diagnose PD. For the speech signals, features from the MDVR-KCL dataset are extracted numerically using 16 feature extraction algorithms and fed to 4 different machine learning algorithms tuned by Grid Search algorithm, and graphically using 5 different techniques and fed to the 8 pretrained CNN structures. The authors propose a new technique in extracting the features from the voice dataset based on the segmentation of variable speech-signal-segment-durations, i.e., the use of different durations in the segmentation phase. Using the proposed technique, 5 datasets with 281 numerical features are generated. Results from different experiments are collected and recorded. For the NewHandPD dataset, the best-reported metric is 99.75% using the VGG19 structure. For the MDVR-KCL dataset, the best-reported metrics are 99.94% using the KNN and SVM ML algorithms and the combined numerical features; and 100% using the combined the mel-specgram graphical features and VGG19 structure. These results are better than other state-of-the-art researches.

Phonological duration differences in quantity languages can be problematic for second language learners whose native language does not use duration contrastively. Recent studies have found improvement in the processing of non-native vowel duration contrasts with the use of listen-and-repeat training, and the current study explores the efficacy of similar methodology on consonant duration contrasts. 18 adult participants underwent two days of listen-and-repeat training with pseudoword stimuli containing either a sibilant or a stop consonant contrast. The results were examined with psychophysiological event-related potentials (mismatch negativity and P3), behavioral discrimination tests and a production task. The results revealed no training-related effects in the event-related potentials or the production task, but behavioral discrimination performance improved. Furthermore, differences emerged between the processing of the two consonant types. The findings suggest that stop consonants are processed more slowly than the sibilants, and the findings are discussed with regard to possible segmentation difficulties.