Papers with speech applications
Prosodic segmentation for parsing spoken dialogue (2021.acl-long)
Copied to clipboard
| Challenge: | Existing parsers struggle to parse spoken dialogue because of disfluencies and unmarked boundaries between sentence-like units (SUs). |
| Approach: | They hypothesize that prosody affects a parser that receives an entire dialogue turn as input, instead of gold standard pre-segmented SUs. |
| Outcome: | The proposed model performs better than the SU-based model on the English Switchboard corpus despite performing two tasks rather than one, and pitch and intensity features are the most important for this corpus. |
Self-Governing Neural Networks for On-Device Short Text Classification (D18-1)
Copied to clipboard
| Challenge: | Existing deep neural networks have a tiny memory footprint and low computational capacity compared to high performance computing systems such as CPUs, GPUs and TPUs on the cloud. |
| Approach: | They propose on-device self-governing neural networks which learn compact projection vectors with local sensitive hashing. |
| Outcome: | The proposed models perform better on dialog act classification tasks while maintaining high accuracy. |
Self-Governing Neural Networks for On-Device Short Text Classification (D18-1)
Copied to clipboard
| Challenge: | Existing deep neural networks have a tiny memory footprint and low computational capacity compared to high performance computing systems such as CPUs, GPUs and TPUs on the cloud. |
| Approach: | They propose on-device self-governing neural networks which learn compact projection vectors with local sensitive hashing. |
| Outcome: | The proposed models perform better on dialog act classification tasks while maintaining high accuracy. |
Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems (2020.lrec-1)
Copied to clipboard
Fei He, Shan-Hui Cathy Chu, Oddur Kjartansson, Clara Rivera, Anna Katanova, Alexander Gutkin, Isin Demirsahin, Cibu Johny, Martin Jansche, Supheakmungkol Sarin, Knot Pipatsrisawat
| Challenge: | We present free high quality multi-speaker speech corpora for Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu . the datasets are primarily intended for use in text-to-speech applications, such as constructing multilingual voices or language adaptation. |
| Approach: | They present a free high quality multi-speaker speech corpora for Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu . they use it to build a multilingual text-to-speech model that can be scaled to other languages of interest. |
| Outcome: | The proposed model produces good quality voices with MOS > 3.6 for all the languages tested. |