Many automatic speech recognition (ASR) data sets include a single pre-defined test set consisting of one or more speakers whose speech never appears in the training set.This "holdspeaker(s)-out" data partitioning strategy, however, may not be ideal for data sets in which the number of speakers is very small.This study investigates ten different data split methods for five languages with minimal ASR training resources.We find that (1) model performance varies greatly depending on which speaker is selected for testing; (2) the average word error rate (WER) across all held-out speakers is comparable not only to the average WER over multiple random splits but also to any given individual random split; (3) WER is also generally comparable when the data is split heuristically or adversarially; (4) utterance duration and intensity are comparatively more predictive factors of variability regardless of the data split.These results suggest that the widely used holdspeakers-out approach to ASR data partitioning can yield results that do not reflect model performance on unseen data or speakers.Random splits can yield more reliable and generalizable estimates when facing data sparsity.

Abstract We present a syntactic dependency treebank for naturalistic child and child-directed spoken English. Our annotations largely follow the guidelines of the Universal Dependencies project (UD [Zeman et al., 2022]), with detailed extensions to lexical and syntactic structures unique to spontaneous spoken language, as opposed to written texts or prepared speech. Compared to existing UD-style spoken treebanks and other dependency corpora of child-parent interactions specifically, our dataset is much larger (44,744 utterances; 233,907 words) and contains data from 10 children covering a wide age range (18–66 months). We conduct thorough dependency parser evaluations using both graph-based and transition-based parsers, trained on three different types of out-of-domain written texts: news, tweets, and learner data. Out-of-domain parsers demonstrate reasonable performance for both child and parent data. In addition, parser performance for child data increases along children’s developmental paths, especially between 18 and 48 months, and gradually approaches the performance for parent data. These results are further validated with in-domain training.

Languages are classified as low-resource when they lack the quantity of data necessary for training statistical and machine learning tools and models.Causes of resource scarcity vary but can include poor access to technology for developing these resources, a relatively small population of speakers, or a lack of urgency for collecting such resources in bilingual populations where the second language is highresource.As a result, the languages described as low-resource in the literature are as different as Finnish on the one hand, with millions of speakers using it in every imaginable domain, and Seneca, with only a small-handful of fluent speakers using the language primarily in a restricted domain.While issues stemming from the lack of resources necessary to train models unite this disparate group of languages, many other issues cut across the divide between widely-spoken low-resource languages and endangered languages.In this position paper, we discuss the unique technological, cultural, practical, and ethical challenges that researchers and indigenous speech community members face when working together to develop language technology to support endangered language documentation and revitalization.We report the perspectives of language teachers, Master Speakers and elders from indigenous communities, as well as the point of view of academics.We describe an ongoing fruitful collaboration and make recommendations for future partnerships between academic researchers and language community stakeholders.

Difficulties with social aspects of language are among the hallmarks of autism spectrum disorder (ASD). These communication differences are thought to contribute to the challenges that adults with ASD experience when seeking employment, underscoring the need for interventions that focus on improving areas of weakness in pragmatic and social language. In this paper, we describe a transformer-based framework for identifying linguistic features associated with social aspects of communication using a corpus of conversations between adults with and without ASD and neurotypical conversational partners produced while engaging in collaborative tasks. While our framework yields strong accuracy overall, performance is significantly worse for the language of participants with ASD, suggesting that they use a more diverse set of strategies for some social linguistic functions. These results, while showing promise for the development of automated language analysis tools to support targeted language interventions for ASD, also reveal weaknesses in the ability of large contextualized language models to model neuroatypical language.

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation, and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements on several fronts that were made in the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g., missing gender and macrons information. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet.

Abstract Common designs of model evaluation typically focus on monolingual settings, where different models are compared according to their performance on a single data set that is assumed to be representative of all possible data for the task at hand. While this may be reasonable for a large data set, this assumption is difficult to maintain in low-resource scenarios, where artifacts of the data collection can yield data sets that are outliers, potentially making conclusions about model performance coincidental. To address these concerns, we investigate model generalizability in crosslinguistic low-resource scenarios. Using morphological segmentation as the test case, we compare three broad classes of models with different parameterizations, taking data from 11 languages across 6 language families. In each experimental setting, we evaluate all models on a first data set, then examine their performance consistency when introducing new randomly sampled data sets with the same size and when applying the trained models to unseen test sets of varying sizes. The results demonstrate that the extent of model generalization depends on the characteristics of the data set, and does not necessarily rely heavily on the data set size. Among the characteristics that we studied, the ratio of morpheme overlap and that of the average number of morphemes per word between the training and test sets are the two most prominent factors. Our findings suggest that future work should adopt random sampling to construct data sets with different sizes in order to make more responsible claims about model evaluation.

Long videos captured by consumers are typically tied to some of the most important moments of their lives, yet ironically are often the least frequently watched. The time required to initially retrieve and watch sections can be daunting. In this work we propose novel techniques for summarizing and annotating long videos. Existing video summarization techniques focus exclusively on identifying keyframes and subshots, however evaluating these summarized videos is a challenging task. Our work proposes methods to generate visual summaries of long videos, and in addition proposes techniques to annotate and generate textual summaries of the videos using recurrent networks. Interesting segments of long video are extracted based on image quality as well as cinematographic and consumer preference. Key frames from the most impactful segments are converted to textual annotations using sequential encoding and decoding deep learning models. Our summarization technique is benchmarked on the VideoSet dataset, and evaluated by humans for informative and linguistic content. We believe this to be the first fully automatic method capable of simultaneous visual and textual summarization of long consumer videos.

University students in the United States are routinely asked to provide feedback on the quality of the instruction they have received. Such feedback is widely used by university administrators to evaluate teaching ability, despite growing evidence that students assign lower numerical scores to women and people of color, regardless of the actual quality of instruction. In this paper, we analyze students’ written comments on faculty evaluation forms spanning eight years and five STEM disciplines in order to determine whether open-ended comments reflect these same biases. First, we apply sentiment analysis techniques to the corpus of comments to determine the overall affect of each comment. We then use this information, in combination with other features, to explore whether there is bias in how students describe their instructors. We show that while the gender of the evaluated instructor does not seem to affect students’ expressed level of overall satisfaction with their instruction, it does strongly influence the language that they use to describe their instructors and their experience in class.

Human image understanding is reflected by individuals' visual and linguistic behaviors, but the meaningful computational integration and interpretation of their multimodal representations remain a challenge. In this paper, we expand a framework for capturing image-region annotations in dermatology, a domain in which interpreting an image is influenced by experts' visual perception skills, conceptual domain knowledge, and task-oriented goals. Our work explores the hypothesis that eye movements can help us understand experts' perceptual processes and that spoken language descriptions can reveal conceptual elements of image inspection tasks. We cast the problem of meaningfully integrating visual and linguistic data as unsupervised bitext alignment. Using alignment, we create meaningful mappings between physicians' eye movements, which reveal key areas of images, and spoken descriptions of those images. The resulting alignments are then used to annotate image regions with medical concept labels. Our alignment accuracy exceeds baselines using both exact and delayed temporal correspondence. Additionally, comparison of alignment accuracy between a method that identifies clusters in the images based on eye movement vs. a method that identifies clusters using image features suggests that the two approaches perform well on different types of images and concept labels. This suggests that an image annotation framework should integrate information from more than one technique to handle heterogeneous images. We also investigate the performance of the proposed aligner for dermatological primary morphology concept labels, as well as for lesion size or type and distribution-based categories of images.

Among the more recent applications for natural language processing algorithms has been the analysis of spoken language data for diagnostic and remedial purposes, fueled by the demand for simple, objective, and unobtrusive screening tools for neurological disorders such as dementia. The automated analysis of narrative retellings in particular shows potential as a component of such a screening tool since the ability to produce accurate and meaningful narratives is noticeably impaired in individuals with dementia and its frequent precursor, mild cognitive impairment, as well as other neurodegenerative and neurodevelopmental disorders. In this article, we present a method for extracting narrative recall scores automatically and highly accurately from a word-level alignment between a retelling and the source narrative. We propose improvements to existing machine translation-based systems for word alignment, including a novel method of word alignment relying on random walks on a graph that achieves alignment accuracy superior to that of standard expectation maximization-based techniques for word alignment in a fraction of the time required for expectation maximization. In addition, the narrative recall score features extracted from these high-quality word alignments yield diagnostic classification accuracy comparable to that achieved using manually assigned scores and significantly higher than that achieved with summary-level text similarity metrics used in other areas of NLP. These methods can be trivially adapted to spontaneous language samples elicited with non-linguistic stimuli, thereby demonstrating the flexibility and generalizability of these methods.

Deficits in semantic and pragmatic expression are among the hallmark linguistic features of autism. Recent work in deriving computational correlates of clinical spoken language measures has demonstrated the utility of automated linguistic analysis for characterizing the language of children with autism. Most of this research, however, has focused either on young children still acquiring language or on small populations covering a wide age range. In this paper, we extract numerous linguistic features from narratives produced by two groups of children with and without autism from two narrow age ranges. We find that although many differences between diagnostic groups remain constant with age, certain pragmatic measures, particularly the ability to remain on topic and avoid digressions, seem to improve. These results confirm findings reported in the psychology literature while underscoring the need for careful consideration of the age range of the population under investigation when performing clinically oriented computational analysis of spoken language.

Semi-supervised discriminative language modeling uses simulated N-best lists instead of real ASR outputs as its training examples. In this study we apply two techniques in which artificial examples are generated using a WFST and an MT system trained on pairs of reference text and ASR output. We compare the performance of these techniques with the structured prediction and ranking variants of the WER-sensitive perceptron algorithm, and contrast with the supervised case where real ASR outputs are given as input. Choosing Turkish statistical morphs as n-gram features, we analyze the similarities between the hypotheses of these three setups and the number of utilized features. We show that the MT-based system yields the lowest WER, not only because the examples generated by this technique are more effective, but also because the ranking perceptron generalizes better with this setup. When trained on a combination of artificial WFST and MT data, the structured perceptron performs as well on an unseen test set as it does when trained on real ASR output.

We present our work on semi-supervised learning of discriminative language models where the negative examples for sentences in a text corpus are generated using confusion models for Turkish at various granularities, specifically, word, sub-word, syllable and phone levels. We experiment with different language models and various sampling strategies to select competing hypotheses for training with a variant of the perceptron algorithm. We find that morph-based confusion models with a sample selection strategy aiming to match the error distribution of the baseline ASR system gives the best performance. We also observe that substituting half of the supervised training examples with those obtained in a semi-supervised manner gives similar results.

This paper investigates semi-supervised methods for discriminative language modeling, whereby n-best lists are “hallucinated” for given reference text and are then used for training n-gram language models using the perceptron algorithm. We perform controlled experiments on a very strong baseline English CTS system, comparing three methods for simulating ASR output, and compare the results with training with “real” n-best list output from the baseline recognizer. We find that methods based on extracting phrasal cohorts - similar to methods from machine translation for extracting phrase tables - yielded the largest gains of our three methods, achieving over half of the WER reduction of the fully supervised methods.

Discriminative language modeling is a structured classification problem. Log-linear models have been previously used to address this problem. In this paper, the standard dot-product feature representation used in log-linear models is replaced by a non-linear function parameterized by a neural network. Embeddings are learned for each word and features are extracted automatically through the use of convolutional layers. Experimental results show that as a stand-alone model the continuous space model yields significantly lower word error rate (1% absolute), while having a much more compact parameterization (60%-90% smaller). If the baseline scores are combined, our approach performs equally well.

We analyzed the prosody of children with Autism Spectrum Disorder, Developmental Language Disorder, and typical development in conversational speech, using the CSLU ADOS speech corpus. We found several significant differences in the pitch characteristics of these diagnostic groups, and report automatic classification utilizing these features that are well above chance level. We show that the choice of pitch tracker, its parameters, and the pitch correction method can substantially affect the results, thus the scientific relevance of studies on prosody, and may be one of the reasons for conflicting findings.

We present an end-to-end system for automatically scoring spoken responses to a narrative recall test administered to seniors when screening for cognitive impairment. In Wechsler Logical Memory (WLM) test, a patient listens to a brief narrative, then retells the story once immediately and again after a brief delay. We transcribe the retellings automatically using an ASR system, align the transcripts to the source narrative, extract features that replicate the standard clinical scoring method, and then use the features for automatic assessment using a classifier. On a test corpus of 72 subjects, we empirically evaluate different ASR adaptation strategies and analyze the errors with respect to clinical assessment. Despite imperfect recognition, the system presented here yields classification accuracy comparable to that of manually assigned scores. Our results show that automatic assessment of neuropsychological tests such as the WLM is practical for screening large cohorts. Index Terms: clinical diagnostics, classifying mild cognitive impairment

Narrative recall tasks are commonly included in neurological examinations, as deficits in narrative memory are associated with disorders such as Alzheimer's dementia. We explore methods for automatically scoring narrative retellings via alignment to a source narrative. Standard alignment methods, designed for large bilingual corpora for machine translation, yield high alignment error rates (AER) on our small monolingual corpora. We present modifications to these methods that obtain a decrease in AER, an increase in scoring accuracy, and diagnostic classification performance comparable to that of manual methods, thus demonstrating the utility of these techniques for this task and other tasks relying on monolingual alignments.

Poor narrative memory is associated with a variety of neurodegenerative and developmental disorders, such as autism and Alzheimer’s related dementia. Hence, narrative recall tasks are included in most standard neurological examinations. In this paper, we explore methods for automatically assessing the quality of retellings via alignment to the original narrative. Word alignments serve both to automate manual scoring and to derive other features related to narrative coherence that can be used for diagnostic classification. Despite relatively high word alignment error rates, the automatic alignments provide sufficient information to achieve nearly as accurate diagnostic classification as manual scores. Furthermore, additional features that become available with alignment provide utility in classifying subject groups. While the additional features we explore here did not provide additive gains in accuracy, they point the way to the development of many potentially useful features in this domain.