82 research outputs found
Customization of Automatic Speech Recognition Engines for Rare Word Detection Without Costly Model Re-Training
Thanks to Alexa, Siri or Google Assistant automatic speech recognition (ASR) has changed our daily life during the last decade. Prototypic applications in the air traffic management (ATM) domain are available. Recently pre-filling radar label entries by ASR support has reached the technology readiness level before industrialization (TRL6). However, seldom spoken and airspace related words relevant in the ATM context remain a challenge for sophisticated applications. Open-source ASR toolkits or large pre-trained models for experts - allowing to tailor ASR to new domains - can be exploited with a typical constraint on availability of certain amount of domain specific training data, i.e., typically transcribed speech for adapting acoustic and/or language models. In general, it is sufficient for a "universal" ASR engine to reliably recognize a few hundred words that form the vocabulary of the voice communications between air traffic controllers and pilots. However, for each airport some hundred dependent words that are seldom spoken need to be integrated. These challenging word entities comprise special airline designators and waypoint names like "dexon" or "burok", which only appear in a specific region. When used, they are highly informative and thus require high recognition accuracies. Allowing plug and play customization with a minimum expert manipulation assumes that no additional training is required, i.e., fine-tuning the universal ASR. This paper presents an innovative approach to automatically integrate new specific word entities to the universal ASR system. The recognition rate of these region-specific word entities with respect to the universal ASR increases by a factor of 6
ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications
Personal assistants, automatic speech recognizers and dialogue understanding
systems are becoming more critical in our interconnected digital world. A clear
example is air traffic control (ATC) communications. ATC aims at guiding
aircraft and controlling the airspace in a safe and optimal manner. These
voice-based dialogues are carried between an air traffic controller (ATCO) and
pilots via very-high frequency radio channels. In order to incorporate these
novel technologies into ATC (low-resource domain), large-scale annotated
datasets are required to develop the data-driven AI systems. Two examples are
automatic speech recognition (ASR) and natural language understanding (NLU). In
this paper, we introduce the ATCO2 corpus, a dataset that aims at fostering
research on the challenging ATC field, which has lagged behind due to lack of
annotated data. The ATCO2 corpus covers 1) data collection and pre-processing,
2) pseudo-annotations of speech data, and 3) extraction of ATC-related named
entities. The ATCO2 corpus is split into three subsets. 1) ATCO2-test-set
corpus contains 4 hours of ATC speech with manual transcripts and a subset with
gold annotations for named-entity recognition (callsign, command, value). 2)
The ATCO2-PL-set corpus consists of 5281 hours of unlabeled ATC data enriched
with automatic transcripts from an in-domain speech recognizer, contextual
information, speaker turn information, signal-to-noise ratio estimate and
English language detection score per sample. Both available for purchase
through ELDA at http://catalog.elra.info/en-us/repository/browse/ELRA-S0484. 3)
The ATCO2-test-set-1h corpus is a one-hour subset from the original test set
corpus, that we are offering for free at https://www.atco2.org/data. We expect
the ATCO2 corpus will foster research on robust ASR and NLU not only in the
field of ATC communications but also in the general research community.Comment: Manuscript under review; The code will be available at
https://github.com/idiap/atco2-corpu
Alternative Pseudo-Labeling for Semi-Supervised Automatic Speech Recognition
When labeled data is insufficient, semi-supervised learning with the
pseudo-labeling technique can significantly improve the performance of
automatic speech recognition. However, pseudo-labels are often noisy,
containing numerous incorrect tokens. Taking noisy labels as ground-truth in
the loss function results in suboptimal performance. Previous works attempted
to mitigate this issue by either filtering out the nosiest pseudo-labels or
improving the overall quality of pseudo-labels. While these methods are
effective to some extent, it is unrealistic to entirely eliminate incorrect
tokens in pseudo-labels. In this work, we propose a novel framework named
alternative pseudo-labeling to tackle the issue of noisy pseudo-labels from the
perspective of the training objective. The framework comprises several
components. Firstly, a generalized CTC loss function is introduced to handle
noisy pseudo-labels by accepting alternative tokens in the positions of
incorrect tokens. Applying this loss function in pseudo-labeling requires
detecting incorrect tokens in the predicted pseudo-labels. In this work, we
adopt a confidence-based error detection method that identifies the incorrect
tokens by comparing their confidence scores with a given threshold, thus
necessitating the confidence score to be discriminative. Hence, the second
proposed technique is the contrastive CTC loss function that widens the
confidence gap between the correctly and incorrectly predicted tokens, thereby
improving the error detection ability. Additionally, obtaining satisfactory
performance with confidence-based error detection typically requires extensive
threshold tuning. Instead, we propose an automatic thresholding method that
uses labeled data as a proxy for determining the threshold, thus saving the
pain of manual tuning.Comment: Accepted by IEEE/ACM Transactions on Audio, Speech and Language
Processing (TASLP), 202
Lessons Learned in ATCO2: 5000 hours of Air Traffic Control Communications for Robust Automatic Speech Recognition and Understanding
Voice communication between air traffic controllers (ATCos) and pilots is
critical for ensuring safe and efficient air traffic control (ATC). This task
requires high levels of awareness from ATCos and can be tedious and
error-prone. Recent attempts have been made to integrate artificial
intelligence (AI) into ATC in order to reduce the workload of ATCos. However,
the development of data-driven AI systems for ATC demands large-scale annotated
datasets, which are currently lacking in the field. This paper explores the
lessons learned from the ATCO2 project, a project that aimed to develop a
unique platform to collect and preprocess large amounts of ATC data from
airspace in real time. Audio and surveillance data were collected from publicly
accessible radio frequency channels with VHF receivers owned by a community of
volunteers and later uploaded to Opensky Network servers, which can be
considered an "unlimited source" of data. In addition, this paper reviews
previous work from ATCO2 partners, including (i) robust automatic speech
recognition, (ii) natural language processing, (iii) English language
identification of ATC communications, and (iv) the integration of surveillance
data such as ADS-B. We believe that the pipeline developed during the ATCO2
project, along with the open-sourcing of its data, will encourage research in
the ATC field. A sample of the ATCO2 corpus is available on the following
website: https://www.atco2.org/data, while the full corpus can be purchased
through ELDA at http://catalog.elra.info/en-us/repository/browse/ELRA-S0484. We
demonstrated that ATCO2 is an appropriate dataset to develop ASR engines when
little or near to no ATC in-domain data is available. For instance, with the
CNN-TDNNf kaldi model, we reached the performance of as low as 17.9% and 24.9%
WER on public ATC datasets which is 6.6/7.6% better than "out-of-domain" but
supervised CNN-TDNNf model.Comment: Manuscript under revie
Development of efficient techniques for ASR System for Speech Detection and Recognization system using Gaussian Mixture Model- Universal Background Model
Some practical uses of ASR have been implemented, including the transcription of meetings and the usage of smart speakers. It is the process by which speech waves are transformed into text that allows computers to interpret and act upon human speech. Scalable strategies for developing ASR systems in languages where no voice transcriptions or pronunciation dictionaries exist are the primary focus of this work. We first show that the necessity for voice transcription into the target language can be greatly reduced through cross-lingual acoustic model transfer when phonemic pronunciation lexicons exist in the new language. Afterwards, we investigate three approaches to dealing with languages that lack a pronunciation lexicon. Secondly, we have a look at the efficiency of graphemic acoustic model transfer, which makes it easy to build pronunciation dictionaries. Thesis problems can be solved, in part, by investigating optimization strategies for training on huge corpora (such as GA+HMM and DE+HMM). In the training phase of acoustic modelling, the suggested method is applied to traditional methods. Read speech and HMI voice experiments indicated that while each data augmentation strategy alone did not always increase recognition performance, using all three techniques together did. Power normalised cepstral coefficient (PNCC) features are tweaked somewhat in this work to enhance verification accuracy. To increase speaker verification accuracy, we suggest employing multiple “Gaussian Mixture Model-Universal Background Model (GMM-UBM) and SVM classifiers”. Importantly, pitch shift data augmentation and multi-task training reduced bias by more than 18% absolute compared to the baseline system for read speech, and applying all three data augmentation techniques during fine tuning reduced bias by more than 7% for HMI speech, while increasing recognition accuracy of both native and non-native Dutch speech
Automatic Speech Analysis Framework for ATC Communication in HAAWAII
Over the past years, several SESAR funded exploratory projects focused on bringing speech and language technologies to the Air Traffic Management (ATM) domain and demonstrating their added value through successful applications. Recently ended HAAWAII project developed a generic architecture and framework, which was validated through several tasks such as callsign highlighting, pre-filling radar labels, and readback error detection. The primary goal was to support pilot and air traffic controller communication by deploying Automatic Speech Recognition (ASR) engines. Contextual information (if available) extracted from surveillance data, flight plan data, or previous communication can be exploited via entity boosting to further improve the recognition performance. HAAWAII proposed various design attributes to integrate the ASR engine into the ATM framework, often depending on concrete technical specifics of target air navigation service providers (ANSPs). This paper gives a brief overview and provides an objective assessment of speech processing components developed and integrated into the HAAWAII framework. Specifically, the following tasks are evaluated w.r.t. application domain: (i) speech activity detection, (ii) speaker segmentation and speaker role classification, as well as (iii) ASR. To our best knowledge, HAAWAII framework offers the best performing speech technologies for ATM, reaching high recognition accuracy (i.e., error-correction done by exploiting additional contextual data), robustness (i.e., models developed using large training corpora) and support for rapid domain transfer (i.e., to new ATM sector with minimum investment). Two scenarios provided by ANSPs were used for testing, achieving callsign detection accuracy of about 96% and 95% for NATS and ISAVIA, respectively
A survey on artificial intelligence-based acoustic source identification
The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions
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