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Automated intelligibility assessments can support speech and language therapists in determining the type of dysarthria presented by their clients. Such assessments can also help predict how well a person with dysarthria might cope with a voice interface to assistive technology. Our approach to intelligibility assessment is based on iVectors, a set of measures that capture many aspects of a person's speech, including intelligibility. The major advantage of iVectors is that they compress all acoustic information contained in an utterance into a reduced number of measures, and they are very suitable to be used with simple predictors. We show that intelligibility assessments work best if there is a pre-existing set of words annotated for intelligibility from the speaker to be evaluated, which can be used for training our system. We discuss the implications of our findings for practice. . 2015. Intelligibility assessment and speech recognizer word accuracy rate prediction for dysarthric speakers in a factor analysis subspace.

This paper explores two techniques to improve the performance of text-dependent speaker verification systems based on deep neural networks. Firstly, we propose a general alignment mechanism to keep the temporal structure of each phrase and obtain a supervector with the speaker and phrase information, since both are relevant for a text-dependent verification. As we show, it is possible to use different alignment techniques to replace the global average pooling providing significant gains in performance. Moreover, we also present a novel back-end approach to train a neural network for detection tasks by optimizing the Area Under the Curve (AUC) as an alternative to the usual triplet loss function, so the system is end-to-end, with a cost function close to our desired measure of performance. As we can see in the experimental section, this approach improves the system performance, since our triplet neural network based on an approximation of the AUC (aAUC) learns how to discriminate between pairs of examples from the same identity and pairs of different identities. The different alignment techniques to produce supervectors in addition to the new back-end approach were tested on the RSR2015-Part I database for text-dependent speaker verification, providing competitive results compared to similar size networks using the global average pooling to extract supervectors and using a simple back-end or triplet loss training.The performance in many speaker verification tasks has improved thanks to deep learning advances in signal representations [1,2] or optimization metrics [3,4,5] that have been adapted from the state-of-the-art deep learning face verification systems. In this paper, we propose alternatives to the two following aspects. First, the generation of signal representations or embeddings which preserve the subject identity and uttered phrase. Second, a metric for training the system which, as we will show, is more appropriate for a detection task.In many recent verification systems, deep neural networks (DNNs) are trained for multi-class classification. A common approach is to apply a global average reduction mechanism [1, 2, 6], which produces a vector representing the whole utterance which is called embedding. A simple back-end as a similarity metric can be applied directly for the verification process [7] or more sophisticated methods like the one presented in [8]. However, this approach does not work efficiently in text-dependent tasks since the uttered phrase is a relevant piece of information to correctly determine the identities and the system has to detect a match in the speaker and the phrase to be correct [6,9]. In our previous work [10], we have noted that part of the imprecisions may be derived from the use of the average as a representation of the utterance, and how this problem can be solved by adding a new internal layer into the deep neural network architecture which uses an alignment method to encode the temporal structure of the phrase in a supervector. In this paper, we propose a generalization ...

The IberSpeech-RTVE Challenge presented at IberSpeech 2018 is a new Albayzin evaluation series supported by the Spanish Thematic Network on Speech Technologies (Red Temática en Tecnologías del Habla (RTTH)). That series was focused on speech-to-text transcription, speaker diarization, and multimodal diarization of television programs. For this purpose, the Corporacion Radio Television Española (RTVE), the main public service broadcaster in Spain, and the RTVE Chair at the University of Zaragoza made more than 500 h of broadcast content and subtitles available for scientists. The dataset included about 20 programs of different kinds and topics produced and broadcast by RTVE between 2015 and 2018. The programs presented different challenges from the point of view of speech technologies such as: the diversity of Spanish accents, overlapping speech, spontaneous speech, acoustic variability, background noise, or specific vocabulary. This paper describes the database and the evaluation process and summarizes the results obtained. in Spanish [8][9][10][11], and more recently, the Multi-Genre Broadcast (MGB) Challenge with data in English and Arabic 2 [12][13][14]. In other areas apart from broadcast speech, several evaluation campaigns have been proposed such as the ones organized in the scope of the Zero Resource Speech Challenge [15,16], the TC-STAR evaluation on recordings of the European Parliament's sessions in English and Spanish [5], or the MediaEval evaluation of multimodal search and hyperlinking [17].As a way to measure the performance of different techniques and approaches, in this 2018 edition, the IberSpeech-RTVE Challenge Evaluation campaign was proposed in three different conditions: speech-to-text transcription (STT), speaker diarization (SD), and multimodal diarization (MD). Twenty-two teams registered to the challenge, and eighteen submitted systems in at least one of the three proposed tasks. In this paper, we describe the challenge and the data provided by the organization to the participants. We also provide a description of the systems presented to the evaluation, their results, and a set of conclusions that can be drawn from this evaluation campaign.This paper is organized as follows. In Section 2, the RTVE2018 database is presented. Section 3 describes the three evaluation tasks, speech-to-text transcription, speaker diarization, and multimodal diarization. Section 4 provides a brief description of the main features of the submitted systems. Section 5 presents results, and Section 6 gives conclusions.