Kaldi Speech Recognition Toolkit
The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.
this paper
works it cites
works citing it
node size = global citations · hover for the full title
Citation edge evidence
- Dropout: a simple way to prevent neural networks from overfitting→Kaldi Speech Recognition ToolkitLegacy edge — source detail unavailable
- Librispeech: An ASR corpus based on public domain audio books→Kaldi Speech Recognition ToolkitLegacy edge — source detail unavailable
- SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition→Kaldi Speech Recognition ToolkitLegacy edge — source detail unavailable
- X-Vectors: Robust DNN Embeddings for Speaker Recognition→Kaldi Speech Recognition ToolkitLegacy edge — source detail unavailable
- Listen, attend and spell: A neural network for large vocabulary conversational speech rec…→Kaldi Speech Recognition ToolkitLegacy edge — source detail unavailable
- Towards End-To-End Speech Recognition with Recurrent Neural Networks→Kaldi Speech Recognition ToolkitLegacy edge — source detail unavailable
- Hybrid speech recognition with Deep Bidirectional LSTM→Kaldi Speech Recognition ToolkitLegacy edge — source detail unavailable
- Deep Speech: Scaling up end-to-end speech recognition→Kaldi Speech Recognition ToolkitLegacy edge — source detail unavailable
- Kaldi Speech Recognition Toolkit→Maximum likelihood linear regression for speaker adaptation of continuous density hidden …Legacy edge — source detail unavailable
- Kaldi Speech Recognition Toolkit→Maximum likelihood linear transformations for HMM-based speech recognitionLegacy edge — source detail unavailable
- Deep Speech: Scaling up end-to-end speech recognition→Towards End-To-End Speech Recognition with Recurrent Neural NetworksLegacy edge — source detail unavailable
- Listen, attend and spell: A neural network for large vocabulary conversational speech rec…→Deep Speech: Scaling up end-to-end speech recognitionLegacy edge — source detail unavailable
- Listen, attend and spell: A neural network for large vocabulary conversational speech rec…→Hybrid speech recognition with Deep Bidirectional LSTMLegacy edge — source detail unavailable
- Listen, attend and spell: A neural network for large vocabulary conversational speech rec…→Towards End-To-End Speech Recognition with Recurrent Neural NetworksLegacy edge — source detail unavailable
- SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition→Librispeech: An ASR corpus based on public domain audio booksLegacy edge — source detail unavailable
- SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition→Deep Speech: Scaling up end-to-end speech recognitionLegacy edge — source detail unavailable
- SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition→Towards End-To-End Speech Recognition with Recurrent Neural NetworksLegacy edge — source detail unavailable
- SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition→Listen, attend and spell: A neural network for large vocabulary conversational speech rec…Legacy edge — source detail unavailable
What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Maximum likelihood linear regression for speaker adaptation of continuous density hidden … | 1995 | 2,214 |
| Maximum likelihood linear transformations for HMM-based speech recognition | 1998 | 1,499 |
What cites it, inside the corpus
Links
Download BibTeX · Download RIS · DOI · Open access copy · OpenAlex record
Topics
| Speech Recognition and Synthesis | Computer Science |
| Speech and Audio Processing | Computer Science |
| Music and Audio Processing | Computer Science |
Is this record sound?
complete
Nothing in this record contradicts itself and no field we check is missing.
- supports1 author record(s) attached.
- supports15 reference(s) recorded.
- neutralThe DOI carries no year to check against.
- supportsA title is present.
Provenance
sha256 648f8491aaceaa34…