Real-time integration of dynamic context information for improving automatic speech recognition

Oualil, Youssef; Schulder, Marc; Helmke, Hartmut; Schmidt, Anna Marie; Klakow, Dietrich

doi:10.21437/interspeech.2015-476

Cited by 9 publications

(5 citation statements)

References 5 publications

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“…The work overcame this with the error correction technique which requires the minimally extracted call sign from the utterances. This work strengthens Cordero J et al [7] work by a clustering algorithm which can generate a local context and followed by a string distance algorithm to find the closest match for the whole sign or the rest of call sign part. The International Civil Aviation Organization (ICAO) has defined a phraseology that includes command, phrases, dictionary that should be used for communication.…”

supporting

confidence: 68%

“…Maximum occurrences piggybacking on the repeatability pattern was discussed in the previous section. Quite a few earlier works discussed how to use context to improve the error correction Oualil Y et al 7 , Nguyen V et al [8], Srinivasamurthy A [9]. Context ranges from an airport database to an external system such as radar which has the ground truth of callsigns, frequencies, waypoints, and other named entities in ATC.…”

Section: Co-occurrences Using String Clustering Algorithmsmentioning

confidence: 99%

“…Hence the need for an independent system which does not integrate with other ATC systems is required. While Oualil Y et al [2], Nguyen V et al [3], and Srinivasamurthy A4 applied language model rules to the speech recognition pipeline. Cordero J et al [1] applied it at post processing to determine the call sign and command part of the utterance.…”

mentioning

confidence: 99%

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Minimizing Errors inAir Traffic Speech Using Rule Based Algorithms

Srinivasan,

S R

2023

IJCERT

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The application of automatic speech recognition for air traffic speech has attracted a lot of attention in recent times. The application of machine learning and deep learning models can address the challenges of speech recognition to some extent. This work focuses on understanding the intricacies of domain characteristics and how to leverage them to improve speech recognition. This work focuses on applying rulebased techniques in the post processing phase of speech recognition. The post-processing stage is defined as a stage where a set of algorithms are applied to the decoded output of speech recognition. Multiple techniques, like syntax separation techniques, co-occurrence-based clustering techniques, and string distance algorithms, are discussed in this work. The choice of these algorithms is based on an understanding of the significance of air traffic speech domain characteristics. Machine learning and deep learning models were applied in feature selection, language model generation, or acoustic model generation. Approaches based on rules could be selectively applied as an incremental update to language models or acoustic model generation. This work was able to improve the accuracy by 9% by applying selective algorithms for error detection and correction. Comparisons of different techniques were discussed when multiple techniques were used for clustering and string distance calculations. One of the good observations of this work is that leveraging the characteristics of speech in this domain helped improve accuracy. The improvement in accuracy was seen in two scenarios. One scenario has a complete utterance, and the other scenario has syntax-separated utterances. A rule-based algorithm was proposed for syntax separation.

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confidence: 68%

Section: Co-occurrences Using String Clustering Algorithmsmentioning

confidence: 99%

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Minimizing Errors inAir Traffic Speech Using Rule Based Algorithms

Srinivasan,

S R

2023

IJCERT

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“…Although there is an established standard phraseology for ATC commands, Helmke et al [8] report that >=25% of spoken commands do not follow the standard phraseology. There is heavy mix of general English words that emerge naturally during conversation, and the use of local language dialects is another primary factor in this non-adherence to the standard phraseology.…”

Section: Recognitionmentioning

confidence: 99%

Enhancing Command Recognition in Air Traffic Control Through Advanced Classification Techniques

Srinivasan,

Balasundaram

2024

Transactions on Aerospace Research

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This paper addresses the persistent challenges in speech processing within the Air Traffic Control (ATC) domain, a field where despite extensive research, issues such as handling noisy environments, accented speech, and the need for strict adherence to standard phraseology continue to undermine conventional language models. Our study employs a hybrid approach that integrates syntactic analysis with advanced machine learning classification algorithms – Logistic Regression, Lagrangian Support Vector Machine, and Naïve Bayes. By mixing and matching algorithms tailored for specific aspects of speech processing, our approach moves away from traditional reliance on a singular integrated system, illustrating through rigorous testing with the ATCOSIM dataset that such a multifaceted strategy markedly improves command recognition accuracy and adapts more effectively to the unique linguistic features of ATC speech. Results highlight the superior performance of Logistic Regression across various command recognition categories, pointing towards a promising direction for future advancements in ATC speech recognition technologies aimed at reducing human workload and increasing automation precision. This paper explores the complexities of the required analysis techniques and underscores the necessity of employing diverse algorithms in the processing pipeline to enhance overall system accuracy.

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“…Various works have already investigated context incorporation in the ASR [5,6,7], which marks the prior step in the ATC speech processing pipeline. Two other works of the ATCO2 project [8,9] show that the combination of HCLG and lattice boosting using Kaldi [10], reduces the ATC-ASR errors, especially for the call-signs.…”

Section: Related Workmentioning

confidence: 99%

Call-Sign Recognition and Understanding for Noisy Air-Traffic Transcripts Using Surveillance Information

Blatt

Kocour

Veselý

et al. 2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Air traffic control (ATC) relies on communication via speech between pilot and air-traffic controller (ATCO). The call-sign, as unique identifier for each flight, is used to address a specific pilot by the ATCO. Extracting the call-sign from the communication is a challenge because of the noisy ATC voice channel and the additional noise introduced by the receiver. A low signal-to-noise ratio (SNR) in the speech leads to high word error rate (WER) transcripts. We propose a new callsign recognition and understanding (CRU) system that addresses this issue. The recognizer is trained to identify callsigns in noisy ATC transcripts and convert them into the standard International Civil Aviation Organization (ICAO) format. By incorporating surveillance information, we can multiply the call-sign accuracy (CSA) up to a factor of four. The introduced data augmentation adds additional performance on high WER transcripts and allows the adaptation of the model to unseen airspaces.

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Real-time integration of dynamic context information for improving automatic speech recognition

Cited by 9 publications

References 5 publications

Minimizing Errors inAir Traffic Speech Using Rule Based Algorithms

Minimizing Errors inAir Traffic Speech Using Rule Based Algorithms

Enhancing Command Recognition in Air Traffic Control Through Advanced Classification Techniques

Call-Sign Recognition and Understanding for Noisy Air-Traffic Transcripts Using Surveillance Information

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