70 research outputs found

    Spoken content retrieval: A survey of techniques and technologies

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    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

    Searching Spontaneous Conversational Speech:Proceedings of ACM SIGIR Workshop (SSCS2008)

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

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    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)

    Building competitive direct acoustics-to-word models for English conversational speech recognition

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    Direct acoustics-to-word (A2W) models in the end-to-end paradigm have received increasing attention compared to conventional sub-word based automatic speech recognition models using phones, characters, or context-dependent hidden Markov model states. This is because A2W models recognize words from speech without any decoder, pronunciation lexicon, or externally-trained language model, making training and decoding with such models simple. Prior work has shown that A2W models require orders of magnitude more training data in order to perform comparably to conventional models. Our work also showed this accuracy gap when using the English Switchboard-Fisher data set. This paper describes a recipe to train an A2W model that closes this gap and is at-par with state-of-the-art sub-word based models. We achieve a word error rate of 8.8%/13.9% on the Hub5-2000 Switchboard/CallHome test sets without any decoder or language model. We find that model initialization, training data order, and regularization have the most impact on the A2W model performance. Next, we present a joint word-character A2W model that learns to first spell the word and then recognize it. This model provides a rich output to the user instead of simple word hypotheses, making it especially useful in the case of words unseen or rarely-seen during training.Comment: Submitted to IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 201

    Out-of-vocabulary spoken term detection

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    Spoken term detection (STD) is a fundamental task for multimedia information retrieval. A major challenge faced by an STD system is the serious performance reduction when detecting out-of-vocabulary (OOV) terms. The difficulties arise not only from the absence of pronunciations for such terms in the system dictionaries, but from intrinsic uncertainty in pronunciations, significant diversity in term properties and a high degree of weakness in acoustic and language modelling. To tackle the OOV issue, we first applied the joint-multigram model to predict pronunciations for OOV terms in a stochastic way. Based on this, we propose a stochastic pronunciation model that considers all possible pronunciations for OOV terms so that the high pronunciation uncertainty is compensated for. Furthermore, to deal with the diversity in term properties, we propose a termdependent discriminative decision strategy, which employs discriminative models to integrate multiple informative factors and confidence measures into a classification probability, which gives rise to minimum decision cost. In addition, to address the weakness in acoustic and language modelling, we propose a direct posterior confidence measure which replaces the generative models with a discriminative model, such as a multi-layer perceptron (MLP), to obtain a robust confidence for OOV term detection. With these novel techniques, the STD performance on OOV terms was improved substantially and significantly in our experiments set on meeting speech data

    Segmentation, Diarization and Speech Transcription: Surprise Data Unraveled

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    In this thesis, research on large vocabulary continuous speech recognition for unknown audio conditions is presented. For automatic speech recognition systems based on statistical methods, it is important that the conditions of the audio used for training the statistical models match the conditions of the audio to be processed. Any mismatch will decrease the accuracy of the recognition. If it is unpredictable what kind of data can be expected, or in other words if the conditions of the audio to be processed are unknown, it is impossible to tune the models. If the material consists of `surprise data' the output of the system is likely to be poor. In this thesis methods are presented for which no external training data is required for training models. These novel methods have been implemented in a large vocabulary continuous speech recognition system called SHoUT. This system consists of three subsystems: speech/non-speech classification, speaker diarization and automatic speech recognition. The speech/non-speech classification subsystem separates speech from silence and unknown audible non-speech events. The type of non-speech present in audio recordings can vary from paper shuffling in recordings of meetings to sound effects in television shows. Because it is unknown what type of non-speech needs to be detected, it is not possible to train high quality statistical models for each type of non-speech sound. The speech/non-speech classification subsystem, also called the speech activity detection subsystem, does not attempt to classify all audible non-speech in a single run. Instead, first a bootstrap speech/silence classification is obtained using a standard speech activity component. Next, the models for speech, silence and audible non-speech are trained on the target audio using the bootstrap classification. This approach makes it possible to classify speech and non-speech with high accuracy, without the need to know what kinds of sound are present in the audio recording. Once all non-speech is filtered out of the audio, it is the task of the speaker diarization subsystem to determine how many speakers occur in the recording and exactly when they are speaking. The speaker diarization subsystem applies agglomerative clustering to create clusters of speech fragments for each speaker in the recording. First, statistical speaker models are created on random chunks of the recording and by iteratively realigning the data, retraining the models and merging models that represent the same speaker, accurate speaker models are obtained for speaker clustering. This method does not require any statistical models developed on a training set, which makes the diarization subsystem insensitive for variation in audio conditions. Unfortunately, because the algorithm is of complexity O(n3)O(n^3), this clustering method is slow for long recordings. Two variations of the subsystem are presented that reduce the needed computational effort, so that the subsystem is applicable for long audio recordings as well. The automatic speech recognition subsystem developed for this research, is based on Viterbi decoding on a fixed pronunciation prefix tree. Using the fixed tree, a flexible modular decoder could be developed, but it was not straightforward to apply full language model look-ahead efficiently. In this thesis a novel method is discussed that makes it possible to apply language model look-ahead effectively on the fixed tree. Also, to obtain higher speech recognition accuracy on audio with unknown acoustical conditions, a selection from the numerous known methods that exist for robust automatic speech recognition is applied and evaluated in this thesis. The three individual subsystems as well as the entire system have been successfully evaluated on three international benchmarks. The diarization subsystem has been evaluated at the NIST RT06s benchmark and the speech activity detection subsystem has been tested at RT07s. The entire system was evaluated at N-Best, the first automatic speech recognition benchmark for Dutch

    Modelling Semantic Context of OOV Words in Large Vocabulary Continuous Speech Recognition

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    International audienceThe diachronic nature of broadcast news data leads to the problem of Out-Of-Vocabulary (OOV) words in Large Vocabulary Continuous Speech Recognition (LVCSR) systems. Analysis of OOV words reveals that a majority of them are Proper Names (PNs). However PNs are important for automatic indexing of audio-video content and for obtaining reliable automatic transcriptions. In this paper, we focus on the problem of OOV PNs in diachronic audio documents. To enable recovery of the PNs missed by the LVCSR system, relevant OOV PNs are retrieved by exploiting the semantic context of the LVCSR transcriptions. For retrieval of OOV PNs, we explore topic and semantic context derived from Latent Dirichlet Allocation (LDA) topic models, continuous word vector representations and the Neural Bag-of-Words (NBOW) model which is capable of learning task specific word and context representations. We propose a Neural Bag-of-Weighted Words (NBOW2) model which learns to assign higher weights to words that are important for retrieval of an OOV PN. With experiments on French broadcast news videos we show that the NBOW and NBOW2 models outperform the methods based on raw embeddings from LDA and Skip-gram models. Combining the NBOW and NBOW2 models gives a faster convergence during training. Second pass speech recognition experiments, in which the LVCSR vocabulary and language model are updated with the retrieved OOV PNs, demonstrate the effectiveness of the proposed context models
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