Update README.md

README.md

@@ -9,19 +9,20 @@ A summary on our attempts at using Deep Learning approached for Emotional Text t
 
 Even though our project was cetered around using HMMs for Speech synthsis, we were encouraged to try out alternate approaches for the same. Hence, we started this separate thread of DL Approaches for Emotional TTS.
 
-### Preliminaries
+## Preliminaries
 
 Before we start out with Deep Learning based approaches for TTS, it is essential to learn about the data resources and the possible models for that have been developed for neural TTS and can be adapted for Emotional TTS. In this section, we briefly talk about the resources we explored in terms of both datasets and existing neural architectures, with a link to more elaborate resources.
 
-#### Datasets
+### Datasets
 
 | Dataset | No. of Speakers | Emotions | No. of utterances | No. of unique prompts | Duration | Language | Comments | Pros | Cons|
 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- |
 | **[RAVDESS](https://zenodo.org/record/1188976#.Xqw8ntMvPBI)** | 24 (12 female, 12 male) | 8 (calm, neutral, happy, sad, angry, fearful, surprise, and disgust) | 1440 | 2 | ~1 hour | English | This dataset consists of 24 actors (12 female, 12 male), speaking in 8 emotions (calm, neutral, happy, sad, angry, fearful, surprise, and disgust). Each speaker has 4 utterances for neutral emotion and 8 utterances for all other emotions, leading to 60 utterances per speaker | Easily available, smaller dataset. | Very limited utterances, poor vocabulary, same utterance in different voices |
 | **[EMOV-DB](https://github.com/numediart/EmoV-DB)** | 5 (3 male, 2 female) | 5 (neutral, amused, angry sleepy, disgust) |  6914 (1568, 1315, 1293, 1720, 1018) | 1150 | ~7 hours | English, French (1 male speaker) |An attempt at a large scale corpus for Emotional speech. The Amused emotion contains non-verbal cues like chuckling, etc. which do not show up in the transcript. Similarly, Sleepiness has yawning sounds. | Only large scale emotional corpus that we found freely available. | Emotions covered are not very standard. The non-verbal cues make synthsis difficult. Also, not all emotions are available for all speakers. |
+| **[LJ Speech](https://keithito.com/LJ-Speech-Dataset/)** | 1 (1 female) | NA (can be considered neutral) | 13100 | 13100 | 	23 hours 55 minutes 17 seconds | English | This is one of the largest corpuses for speech generation, with a rich vocabulary of over ~14k unique words. The sentences are taken from 7 non-fiction books. | Large scale corpus. Rich vocabulary. Abbreviations in text are expanded in speech. | No emotions. | 
 | **[IEMOCAP](https://sail.usc.edu/iemocap/)** | 10 (5 female, 5 male) | 9 (anger, happiness, excitement, sadness, frustration, fear, surprise, other and neutral state) | 10039 | NA | 12.5 hours | English |This dataset consists of 10 actors (5 male; 5 female) of multi-modal dyadic conversations, which were scripted and enacted by a group of actors. | Variety of utterances, rich vocabulary, multi-modal input, emotion annotations | The access is very restricted and upon being granted access, we got a corpus archive file. |
 
-#### Relevant literature
+### Relevant literature
 
 - **[Tacotron: Towards End-to-End Speech Synthesis](https://arxiv.org/abs/1703.10135)**
   - An extremely influential paper in the are of Neural Text-to-speech. The idea can be abstracted to a simple encoder-decoder network, that takes as input the ground-truth audio and textual transcript. 
@@ -33,13 +34,15 @@ Before we start out with Deep Learning based approaches for TTS, it is essential
   - The core idea was to improve the expressiveness of the generated speech, by incorporating "Style Tokens" which was basically an additional embedding layer for the ground-truth audio, which was used to condition the generated audio, so that transfer of "prosodic features" could occur. 
   - We also explored this model, and presented it for a class lecture. [\[slides\]](https://docs.google.com/presentation/d/1aug9OmIrd8nDY4BmyIK_z65D9DKP5cbrjgX4aDBngFU/edit?usp=sharing)
   - However, we did not explore this as extensively as the Tacotron, as it took a lot of time and resources to train.
-- **[Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention](https://arxiv.org/abs/1710.08969)**: 
+- **[Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention](https://arxiv.org/abs/1710.08969)**
   - This work aimed at more efficient text-to-speech generation by using fully convolutional layers with guided attention.
   - We came across this work while looking for resources for efficient TTS systems that could be fine-tunes with low amount of data.
 
 There are many more relevant papers that build up on the Vanilla Tacotron model. However, for the scope of our project, we restricted ourselves to these three papers.
 
+## Approaches explored
 
+### Approach 1: Fine-tuning a Vanilla Tacotron model that was pre-trained on LJ-Speech
 
-### Approach 1
+Our first approach was to train a vanilla Tacotron model from scratch on just one emotion (say, anger) and see if the g