Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Towards Zero-Shot Learning for Automatic Phonemic Transcription
Authors: Xinjian Li, Siddharth Dalmia, David Mortensen, Juncheng Li, Alan Black, Florian Metze8261-8268
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our model by training it using 13 languages and testing it using 7 unseen languages. We find that it achieves 7.7% better phoneme error rate on average over a standard multilingual model. |
| Researcher Affiliation | Academia | Xinjian Li, Siddharth Dalmia, David R. Mortensen, Juncheng Li, Alan W Black, Florian Metze Language Technologies Institute, School of Computer Science Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1: A simple algorithm to assign attributes to phonemes |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about the release of its source code. |
| Open Datasets | Yes | We prepare two datasets for this experiment. The training set consists of 17 corpora from 13 languages, and the test set is composed of corpora from 7 different languages. ... Details regarding each corpus and each language are provided in Table 1. |
| Dataset Splits | Yes | We note that 5 percent of the entire corpus was used as the validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions using the "EESEN framework" and "Epitran" but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | The input feature is 40 dimension high-resolution MFCCs, the encoder is a 5 layer Bidirectional LSTM model, each layer having 320 cells. ... We train the acoustic model with stochastic gradient descent, using a learning rate of 0.005. |