To assess such benefits with speech tests, one needs tests that are able to detect relevant differences in perception. The results allow the determination of a patient’s speech perception ability and can help determine the potential benefits of hearing aids or cochlear implants. Speech-in-noise testing is a powerful tool for both clinical audiology and audiological research. The work highlights the possibility that existing speech tests might be improved by selecting sentences with a steep psychometric function. This finding indicates that phonemic occurrence is not a critical factor. However, in our material, the median phonemic occurrence remained close to that of the original test. Random selection may interfere with a representative occurrence of phonemes. Therefore, the measurement procedure was changed to randomly select the sentences during testing. The resulting subset did not allow the construction of enough balanced test lists. The calculation showed that the slope increased from 10.2%/dB to 13.7%/dB. Subsequently, the steepness of the psychometric function for this selection was calculated from the remaining (unused) second half of the data set. Based on half of the data set, first the sentences (140 out of 311) with a similar speech reception threshold and with the steepest psychometric function (≥9.7%/dB) were selected. We took data from a previous multicenter evaluation of the Dutch matrix test (45 normal-hearing listeners). The objective is to show if the steepness of the psychometric function of an existing matrix test can be increased by selecting a homogeneous subset of recordings with the steepest sentence-based psychometric functions. For existing tests, it would be beneficial if it were possible to further optimize the available materials by increasing the function’s steepness. For an accurate measurement, a steep psychometric function of the speech materials is required. Additional WER/CER metrics corresponding to the other models and datasets can be found in Appendix D.1, D.2, and D.4 of the paper, as well as the BLEU (Bilingual Evaluation Understudy) scores for translation in Appendix D.3.Matrix tests are available for speech recognition testing in many languages. The figure below shows a performance breakdown of large-v3 and large-v2 models by language, using WERs (word error rates) or CER (character error rates, shown in Italic) evaluated on the Common Voice 15 and Fleurs datasets. Whisper's performance varies widely depending on the language. We observed that the difference becomes less significant for the small.en and medium.en models. en models for English-only applications tend to perform better, especially for the tiny.en and base.en models. Below are the names of the available models and their approximate memory requirements and inference speed relative to the large model actual speed may vary depending on many factors including the available hardware. There are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Pip install setuptools-rust Available models and languages You can download and install (or update to) the latest release of Whisper with the following command: The codebase also depends on a few Python packages, most notably OpenAI's tiktoken for their fast tokenizer implementation. We used Python 3.9.9 and PyTorch 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.8-3.11 and recent PyTorch versions. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets. These tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing a single model to replace many stages of a traditional speech-processing pipeline. ApproachĪ Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification. Whisper is a general-purpose speech recognition model.
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