| copyright | lastupdated | subcollection | ||
|---|---|---|---|---|
|
2025-02-05 |
speech-to-text |
{{site.data.keyword.attribute-definition-list}}
{: #science}
{{site.data.keyword.IBM}} has been at the forefront of speech recognition research since the early 1960s and continues this rich tradition of research and development with the {{site.data.keyword.speechtotextfull}} service. {: shortdesc}
{{site.data.keyword.IBM_notm}} has demonstrated industry-record speech recognition accuracy on the public benchmark data sets for Conversational Telephone Speech (CTS) and Broadcast News (BN) transcription. {{site.data.keyword.IBM_notm}} leveraged neural networks for language modeling in addition to demonstrating the effectiveness of acoustic modeling.
The following announcements summarize {{site.data.keyword.IBM_notm}}'s recent speech recognition accomplishments:
- Reaching new records in speech recognition{: external}
- {{site.data.keyword.IBM_notm}} Breaks Industry Record for Conversational Speech Recognition by Extending Deep Learning Technologies{: external}
- {{site.data.keyword.IBM_notm}} Sets New Transcription Performance Milestone on Automatic Broadcast News Captioning{: external}
- IBM Research AI Advances Speaker Diarization in Real Use Cases{: external}
- Advancing RNN Transducer Technology for Speech Recognition{: external}
These accomplishments contribute to further advance {{site.data.keyword.IBM_notm}}'s speech services. Recent ideas that best fit the cloud-based {{site.data.keyword.speechtotextshort}} service include
- For language modeling, {{site.data.keyword.IBM_notm}} leverages a neural network-based language model to generate training text.
- For acoustic modeling, {{site.data.keyword.IBM_notm}} uses a fairly compact model to accommodate the resource limitations of the cloud. To train this compact model, {{site.data.keyword.IBM_notm}} uses "teacher-student training / knowledge distillation." Large and strong neural networks such as Long Short-Term Memory (LSTM), VGG, and the Residual Network (ResNet) are first trained. The output of these networks is then used as teacher signals to train a compact model for actual deployment.
To further push the envelope, {{site.data.keyword.IBM_notm}} also focuses on end-to-end modeling. For example, it has established a strong modeling pipeline for direct acoustic-to-word models that it is now further improving. It is also making efforts to create compact end-to-end models for future deployment on the cloud.