469 research outputs found
Fast and Accurate OOV Decoder on High-Level Features
This work proposes a novel approach to out-of-vocabulary (OOV) keyword search
(KWS) task. The proposed approach is based on using high-level features from an
automatic speech recognition (ASR) system, so called phoneme posterior based
(PPB) features, for decoding. These features are obtained by calculating
time-dependent phoneme posterior probabilities from word lattices, followed by
their smoothing. For the PPB features we developed a special novel very fast,
simple and efficient OOV decoder. Experimental results are presented on the
Georgian language from the IARPA Babel Program, which was the test language in
the OpenKWS 2016 evaluation campaign. The results show that in terms of maximum
term weighted value (MTWV) metric and computational speed, for single ASR
systems, the proposed approach significantly outperforms the state-of-the-art
approach based on using in-vocabulary proxies for OOV keywords in the indexed
database. The comparison of the two OOV KWS approaches on the fusion results of
the nine different ASR systems demonstrates that the proposed OOV decoder
outperforms the proxy-based approach in terms of MTWV metric given the
comparable processing speed. Other important advantages of the OOV decoder
include extremely low memory consumption and simplicity of its implementation
and parameter optimization.Comment: Interspeech 2017, August 2017, Stockholm, Sweden. 201
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
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)
On the Effectiveness of Neural Text Generation based Data Augmentation for Recognition of Morphologically Rich Speech
Advanced neural network models have penetrated Automatic Speech Recognition
(ASR) in recent years, however, in language modeling many systems still rely on
traditional Back-off N-gram Language Models (BNLM) partly or entirely. The
reason for this are the high cost and complexity of training and using neural
language models, mostly possible by adding a second decoding pass (rescoring).
In our recent work we have significantly improved the online performance of a
conversational speech transcription system by transferring knowledge from a
Recurrent Neural Network Language Model (RNNLM) to the single pass BNLM with
text generation based data augmentation. In the present paper we analyze the
amount of transferable knowledge and demonstrate that the neural augmented LM
(RNN-BNLM) can help to capture almost 50% of the knowledge of the RNNLM yet by
dropping the second decoding pass and making the system real-time capable. We
also systematically compare word and subword LMs and show that subword-based
neural text augmentation can be especially beneficial in under-resourced
conditions. In addition, we show that using the RNN-BNLM in the first pass
followed by a neural second pass, offline ASR results can be even significantly
improved.Comment: 8 pages, 2 figures, accepted for publication at TSD 202
Fuzzy reasoning in confidence evaluation of speech recognition
Confidence measures represent a systematic way to express reliability of speech recognition results. A common approach to confidence measuring is to take profit of the information that several recognition-related features offer and to combine them, through a given compilation mechanism , into a more effective way to distinguish between correct and incorrect recognition results. We propose to use a fuzzy reasoning scheme to perform the information compilation step. Our approach opposes the previously proposed ones because ours treats the uncertainty of recognition hypotheses in terms ofPeer ReviewedPostprint (published version
Core Building Blocks: Next Gen Geo Spatial GPT Application
This paper proposes MapGPT which is a novel approach that integrates the
capabilities of language models, specifically large language models (LLMs),
with spatial data processing techniques. This paper introduces MapGPT, which
aims to bridge the gap between natural language understanding and spatial data
analysis by highlighting the relevant core building blocks. By combining the
strengths of LLMs and geospatial analysis, MapGPT enables more accurate and
contextually aware responses to location-based queries. The proposed
methodology highlights building LLMs on spatial and textual data, utilizing
tokenization and vector representations specific to spatial information. The
paper also explores the challenges associated with generating spatial vector
representations. Furthermore, the study discusses the potential of
computational capabilities within MapGPT, allowing users to perform geospatial
computations and obtain visualized outputs. Overall, this research paper
presents the building blocks and methodology of MapGPT, highlighting its
potential to enhance spatial data understanding and generation in natural
language processing applications
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