Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

Mispronunciation Detection in Children's Reading of Sentences

This work proposes an approach to automatically parse children’s reading of sentences by detecting word pronunciations and extra content, and to classify words as correctly or incorrectly pronounced. This approach can be directly helpful for automatic assessment of reading level or for automatic reading tutors, where a correct reading must be identified. We propose a first segmentation stage to locate candidate word pronunciations based on allowing repetitions and false starts of a word’s syllables. A decoding grammar based solely on syllables allows silence to appear during a word pronunciation. At a second stage, word candidates are classified as mispronounced or not. The feature that best classifies mispronunciations is found to be the log-likelihood ratio between a free phone loop and a word spotting model in the very close vicinity of the candidate segmentation. Additional features are combined in multi-feature models to further improve classification, including: normalizations of the log-likelihood ratio, derivations from phone likelihoods, and Levenshtein distances between the correct pronunciation and recognized phonemes through two phoneme recognition approaches. Results show that most extra events were detected (close to 2% word error rate achieved) and that using automatic segmentation for mispronunciation classification approaches the performance of manual segmentation. Although the log-likelihood ratio from a spotting approach is already a good metric to classify word pronunciations, the combination of additional features provides a relative reduction of the miss rate of 18% (from 34.03% to 27.79% using manual segmentation and from 35.58% to 29.35% using automatic segmentation, at constant 5% false alarm rate).info:eu-repo/semantics/publishedVersio

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)

Morph-to-word transduction for accurate and efficient automatic speech recognition and keyword search

© 2017 IEEE. Word units are a popular choice in statistical language modelling. For inflective and agglutinative languages this choice may result in a high out of vocabulary rate. Subword units, such as morphs, provide an interesting alternative to words. These units can be derived in an unsupervised fashion and empirically show lower out of vocabulary rates. This paper proposes a morph-to-word transduction to convert morph sequences into word sequences. This enables powerful word language models to be applied. In addition, it is expected that techniques such as pruning, confusion network decoding, keyword search and many others may benefit from word rather than morph level decision making. However, word or morph systems alone may not achieve optimal performance in tasks such as keyword search so a combination is typically employed. This paper proposes a single index approach that enables word, morph and phone searches to be performed over a single morph index. Experiments are conducted on IARPA Babel program languages including the surprise languages of the OpenKWS 2015 and 2016 competitions

Cloud-based Automatic Speech Recognition Systems for Southeast Asian Languages

This paper provides an overall introduction of our Automatic Speech Recognition (ASR) systems for Southeast Asian languages. As not much existing work has been carried out on such regional languages, a few difficulties should be addressed before building the systems: limitation on speech and text resources, lack of linguistic knowledge, etc. This work takes Bahasa Indonesia and Thai as examples to illustrate the strategies of collecting various resources required for building ASR systems.Comment: Published by the 2017 IEEE International Conference on Orange Technologies (ICOT 2017

Language independent and unsupervised acoustic models for speech recognition and keyword spotting

Copyright © 2014 ISCA. Developing high-performance speech processing systems for low-resource languages is very challenging. One approach to address the lack of resources is to make use of data from multiple languages. A popular direction in recent years is to train a multi-language bottleneck DNN. Language dependent and/or multi-language (all training languages) Tandem acoustic models (AM) are then trained. This work considers a particular scenario where the target language is unseen in multi-language training and has limited language model training data, a limited lexicon, and acoustic training data without transcriptions. A zero acoustic resources case is first described where a multilanguage AM is directly applied, as a language independent AM (LIAM), to an unseen language. Secondly, in an unsupervised approach a LIAM is used to obtain hypotheses for the target language acoustic data transcriptions which are then used in training a language dependent AM. 3 languages from the IARPA Babel project are used for assessment: Vietnamese, Haitian Creole and Bengali. Performance of the zero acoustic resources system is found to be poor, with keyword spotting at best 60% of language dependent performance. Unsupervised language dependent training yields performance gains. For one language (Haitian Creole) the Babel target is achieved on the in-vocabulary data

LOW RESOURCE HIGH ACCURACY KEYWORD SPOTTING

Keyword spotting (KWS) is a task to automatically detect keywords of interest in continuous speech, which has been an active research topic for over 40 years. Recently there is a rising demand for KWS techniques in resource constrained conditions. For example, as for the year of 2016, USC Shoah Foundation covers audio-visual testimonies from survivors and other witnesses of the Holocaust in 63 countries and 39 languages, and providing search capability for those testimonies requires substantial KWS technologies in low language resource conditions, as for most languages, resources for developing KWS systems are not as rich as that for English. Despite the fact that KWS has been in the literature for a long time, KWS techniques in resource constrained conditions have not been researched extensively. In this dissertation, we improve KWS performance in two low resource conditions: low language resource condition where language specific data is inadequate, and low computation resource condition where KWS runs on computation constrained devices. For low language resource KWS, we focus on applications for speech data mining, where large vocabulary continuous speech recognition (LVCSR)-based KWS techniques are widely used. Keyword spotting for those applications are also known as keyword search (KWS) or spoken term detection (STD). A key issue for this type of KWS technique is the out-of-vocabulary (OOV) keyword problem. LVCSR-based KWS can only search for words that are defined in the LVCSR's lexicon, which is typically very small in a low language resource condition. To alleviate the OOV keyword problem, we propose a technique named "proxy keyword search" that enables us to search for OOV keywords with regular LVCSR-based KWS systems. We also develop a technique that expands LVCSR's lexicon automatically by adding hallucinated words, which increases keyword coverage and therefore improves KWS performance. Finally we explore the possibility of building LVCSR-based KWS systems with limited lexicon, or even without an expert pronunciation lexicon. For low computation resource KWS, we focus on wake-word applications, which usually run on computation constrained devices such as mobile phones or tablets. We first develop a deep neural network (DNN)-based keyword spotter, which is lightweight and accurate enough that we are able to run it on devices continuously. This keyword spotter typically requires a pre-defined keyword, such as "Okay Google". We then propose a long short-term memory (LSTM)-based feature extractor for query-by-example KWS, which enables the users to define their own keywords