LDM speech synthesis

Speech synthesis using Linear Dynamical Models (LDMs).

The task is broken down into two components:

1. Preparing the dataset: Given a dataset which consists of audio utterances along with their transcriptions, we process the data to be used for training our speech synthesis models. Merlin's front end is a great tool for preprocessing the data.
2. Training the LDMs using the processed data.

Tools required

We used Nick corpus for Hurricane Challenge as our dataset. In order to use this dataset, you need to accept the license and obtain a password first. We use the 'plain' (not lombard) news sentences (named herald_xxx) and the 'plain' Harvard sentences (hvd_xx) of this corpus. However, there are other open-source datasets available which can be used here as well. If you want a dataset without any license issues, CMU SLT arctic dataset would be good starting point. It is however a relatively small dataset. LJ speech is a pretty large dataset for TTS. To prepare the dataset the following tools are used:

1. Festival with unilex lexicon : We use festival as a front-end to get phonetic transcriptions from text. Since the speaker for the dataset is an English speaker, we use unilex lexcion to transcribe rather than the default CMU lexicon. This lexicon is again available only after a license.
2. HMM Toolkit (HTK) : We use HTK to align the phonetic transcriptions with their position in the audio utterance. You might encounter a bug in the source code that needs to be fixed.
3. speech signal processing toolkit (SPTK) : We use SPTK for signal processing of audio data.
4. World vocoder: We use World vocoder to create the output acoustic features. In order to do analysis and synthesis of utterances, we use the binaries compiled in Merlin. After compiling the code provided there, we end up with two binaries analysis and synth which perform those two tasks respectively.

The LDMs are trained using Matlab

The front-end preprocessing can be done using Merlin, so we do not duplicate that here. The steps to preprocessing can be understood using this tutorial. The sample_voice directory contains one example file from nick voice which has been processed to extract all the required features so that you may have a fair idea what do all the features look like. The LDM-TTS code is in ldm_tts directory. Please refer their individual README.md files in both the subdirectories..

To evalute the the generated speech, we use PESQ measure.

Author

Gagandeep Singh