I hear you eat and speak: automatic recognition of eating condition and food type, use-cases, and impact on ASR performance

We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient

Prosody-Based Automatic Segmentation of Speech into Sentences and Topics

A crucial step in processing speech audio data for information extraction, topic detection, or browsing/playback is to segment the input into sentence and topic units. Speech segmentation is challenging, since the cues typically present for segmenting text (headers, paragraphs, punctuation) are absent in spoken language. We investigate the use of prosody (information gleaned from the timing and melody of speech) for these tasks. Using decision tree and hidden Markov modeling techniques, we combine prosodic cues with word-based approaches, and evaluate performance on two speech corpora, Broadcast News and Switchboard. Results show that the prosodic model alone performs on par with, or better than, word-based statistical language models -- for both true and automatically recognized words in news speech. The prosodic model achieves comparable performance with significantly less training data, and requires no hand-labeling of prosodic events. Across tasks and corpora, we obtain a significant improvement over word-only models using a probabilistic combination of prosodic and lexical information. Inspection reveals that the prosodic models capture language-independent boundary indicators described in the literature. Finally, cue usage is task and corpus dependent. For example, pause and pitch features are highly informative for segmenting news speech, whereas pause, duration and word-based cues dominate for natural conversation.Comment: 30 pages, 9 figures. To appear in Speech Communication 32(1-2), Special Issue on Accessing Information in Spoken Audio, September 200

Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech

We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as Statement, Question, Backchannel, Agreement, Disagreement, and Apology. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65% based on errorful, automatically recognized words and prosody, and 71% based on word transcripts, compared to a chance baseline accuracy of 35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling changed

Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State vowel Categorization

Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. The transformation from speaker-dependent to speaker-independent language representations enables speech to be learned and understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitch-independent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

An automatic child-directed speech detector for the study of child language development

http://interspeech2012.org/accepted-abstract.html?id=210In this paper, we present an automatic child-directed speech detection system to be used in the study of child language development. Child-directed speech (CDS) is speech that is directed by caregivers towards infants. It is not uncommon for corpora used in child language development studies to have a combination of CDS and non-CDS. As the size of the corpora used in these studies grow, manual annotation of CDS becomes impractical. Our automatic CDS detector addresses this issue. The focus of this paper is to propose and evaluate different sets of features for the detection of CDS, using several offthe-shelf classifiers. First, we look at the performance of a set of acoustic features. We continue by combining these acoustic features with several linguistic and eventually contextual features. Using the full set of features, our CDS detector was able to correctly identify CDS with an accuracy of.88 and F1 score of.87 using Naive Bayes. Index Terms: motherese, automatic, child-directed speech, infant-directed speech, adult-directed speech, prosody, language development

Language engineering - a champion for European culture

Language is key to culture. It is a direct cultural medium as well as a means of recording and providing access to non-lingual elements of culture. Language is also fundamental to a sense of cultural identity. For this reason, it is vital, in a changing Europe, that we preserve the multi-lingual character of our society in order to move successfully towards closer co-operation at a political, economic, and social level. Language engineering is the application of knowledge of language to the development of computer software which can recognise, understand, interpret, and generate human language in all its forms. The paper provides a high level view of the ‘state of the art’ in language engineering and indicates ways in which it will have a profound impact on our culture in the future. It shows how advances in language engineering are an important aid in maintaining cultural diversity in a multi-lingual European society, while enabling the development of social cohesion across cultural and national divides. It addresses issues raised by the prospect of the Multi-lingual Information Society, including education, human communication with technology and information management, as well as aspects of digital cities such as tele-presence in digital libraries, virtual art galleries and electronic museums. The paper raises the issue of language as a factor in cultural domination, showing the contribution that language engineering can make towards countering it. The paper also raises a number of controversial issues concerning the likely benefits arising from the ways in which language is likely to influence the culture of Europe

Multimodal Data Analysis of Dyadic Interactions for an Automated Feedback System Supporting Parent Implementation of Pivotal Response Treatment

abstract: Parents fulfill a pivotal role in early childhood development of social and communication skills. In children with autism, the development of these skills can be delayed. Applied behavioral analysis (ABA) techniques have been created to aid in skill acquisition. Among these, pivotal response treatment (PRT) has been empirically shown to foster improvements. Research into PRT implementation has also shown that parents can be trained to be effective interventionists for their children. The current difficulty in PRT training is how to disseminate training to parents who need it, and how to support and motivate practitioners after training. Evaluation of the parents’ fidelity to implementation is often undertaken using video probes that depict the dyadic interaction occurring between the parent and the child during PRT sessions. These videos are time consuming for clinicians to process, and often result in only minimal feedback for the parents. Current trends in technology could be utilized to alleviate the manual cost of extracting data from the videos, affording greater opportunities for providing clinician created feedback as well as automated assessments. The naturalistic context of the video probes along with the dependence on ubiquitous recording devices creates a difficult scenario for classification tasks. The domain of the PRT video probes can be expected to have high levels of both aleatory and epistemic uncertainty. Addressing these challenges requires examination of the multimodal data along with implementation and evaluation of classification algorithms. This is explored through the use of a new dataset of PRT videos. The relationship between the parent and the clinician is important. The clinician can provide support and help build self-efficacy in addition to providing knowledge and modeling of treatment procedures. Facilitating this relationship along with automated feedback not only provides the opportunity to present expert feedback to the parent, but also allows the clinician to aid in personalizing the classification models. By utilizing a human-in-the-loop framework, clinicians can aid in addressing the uncertainty in the classification models by providing additional labeled samples. This will allow the system to improve classification and provides a person-centered approach to extracting multimodal data from PRT video probes.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Human factors issues associated with the use of speech technology in the cockpit

The human factors issues associated with the use of voice technology in the cockpit are summarized. The formulation of the LHX avionics suite is described and the allocation of tasks to voice in the cockpit is discussed. State-of-the-art speech recognition technology is reviewed. Finally, a questionnaire designed to tap pilot opinions concerning the allocation of tasks to voice input and output in the cockpit is presented. This questionnaire was designed to be administered to operational AH-1G Cobra gunship pilots. Half of the questionnaire deals specifically with the AH-1G cockpit and the types of tasks pilots would like to have performed by voice in this existing rotorcraft. The remaining portion of the questionnaire deals with an undefined rotorcraft of the future and is aimed at determining what types of tasks these pilots would like to have performed by voice technology if anything was possible, i.e. if there were no technological constraints