What are the current advancements in speech recognition technology for specific purposes (SPS)?
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Current advancements in speech recognition technology for specific purposes (SPS) include the development of lie detection technology in speech . This technology focuses on recognizing lying psychological states through speech signal analysis, utilizing features such as semantic characteristics, prosodic characteristics, resonance peak, and psychoacoustic parameters. Another advancement is the use of triplet loss for alternative feature representation in automatic speech recognition (ASR) . This approach, called TRILL, trains a non-semantic speech representation using self-supervised criteria, resulting in improved acoustic modeling. Additionally, there is a trend towards end-to-end (E2E) modeling for ASR, although hybrid models are still widely used in commercial systems . E2E models achieve state-of-the-art results in ASR accuracy, but practical factors and challenges in deployment hinder their widespread commercialization. Techniques such as architectural changes, speaker adaptation, language model fusion, and model combination have been explored to improve the performance of RNN Transducers (RNN-Ts) in ASR .
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Open access•Posted Content | The provided paper discusses advancements in RNN Transducer technology for speech recognition, including architectural changes, speaker adaptation, language model fusion, and general training techniques. |
06 Jun 2021 74 Citations | The provided paper discusses advancements in RNN Transducer technology for speech recognition, including architectural changes, speaker adaptation, language model fusion, and general training techniques. |
52 Citations | The provided paper does not specifically mention advancements in speech recognition technology for specific purposes (SPS). |
| The provided paper is about lie detection technology in speech, not specifically about advancements in speech recognition technology for specific purposes (SPS). | |
Open access•Posted Content | The provided paper discusses advancements in speech recognition technology using triplet loss for alternative feature representation in acoustic modeling. |
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