Spoken term detection ALBAYZIN 2014 evaluation: overview, systems, results, and discussion

The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1186/s13636-015-0063-8Spoken term detection (STD) aims at retrieving data from a speech repository given a textual representation of the search term. Nowadays, it is receiving much interest due to the large volume of multimedia information. STD differs from automatic speech recognition (ASR) in that ASR is interested in all the terms/words that appear in the speech data, whereas STD focuses on a selected list of search terms that must be detected within the speech data. This paper presents the systems submitted to the STD ALBAYZIN 2014 evaluation, held as a part of the ALBAYZIN 2014 evaluation campaign within the context of the IberSPEECH 2014 conference. This is the first STD evaluation that deals with Spanish language. The evaluation consists of retrieving the speech files that contain the search terms, indicating their start and end times within the appropriate speech file, along with a score value that reflects the confidence given to the detection of the search term. The evaluation is conducted on a Spanish spontaneous speech database, which comprises a set of talks from workshops and amounts to about 7 h of speech. We present the database, the evaluation metrics, the systems submitted to the evaluation, the results, and a detailed discussion. Four different research groups took part in the evaluation. Evaluation results show reasonable performance for moderate out-of-vocabulary term rate. This paper compares the systems submitted to the evaluation and makes a deep analysis based on some search term properties (term length, in-vocabulary/out-of-vocabulary terms, single-word/multi-word terms, and in-language/foreign terms).This work has been partly supported by project CMC-V2 (TEC2012-37585-C02-01) from the Spanish Ministry of Economy and Competitiveness. This research was also funded by the European Regional Development Fund, the Galician Regional Government (GRC2014/024, “Consolidation of Research Units: AtlantTIC Project” CN2012/160)

The CALO meeting speech recognition and understanding system

ABSTRACT The CALO Meeting Assistant provides for distributed meeting capture, annotation, automatic transcription and semantic analysis of multi-party meetings, and is part of the larger CALO personal assistant system. This paper summarizes the CALO-MA architecture and its speech recognition and understanding components, which include realtime and offline speech transcription, dialog act segmentation and tagging, question-answer pair identification, action item recognition, and summarization

Development of a conversational telephone speech recognizer for Levantine Arabic

Many languages, including Arabic, are characterized by a wide variety of different dialects that often differ strongly from each other. When developing speech technology for dialect-rich languages, the portability and reusability of data, algorithms, and system components becomes extremely important. In this paper, we describe the development of a large-vocabulary speech recognition system for Levantine Arabic, which was a new dialectal recognition task for our existing system. We discuss the dialect-specific modeling choices (grapheme vs. phoneme based acoustic models, automatic vowelization techniques, and morphological language models) and investigate to what extent techniques previously tested on other languages are portable to the present task. We present stateof-the-ar

Towards language independent acoustic modeling

We describe procedures and experimental results using speech from diverse source languages to build an ASR system for a single target language. This work is intended to improve ASR in languages for which large amounts of training data are not available. We have developed both knowledge based and automatic methods to map phonetic units from the source languages to the target language. We employed HMM adaptation techniques and Discriminative Model Combination to combine acoustic models from the individual source languages for recognition of speech in the target language. Experiments are described in which Czech Broadcast News is transcribed using acoustic models trained from small amounts of Czech read speech augmented by English, Spanish, Russian, and Mandarin acoustic models