---
language:
- en
license: cc-by-nc-4.0
size_categories:
- 1K<n<10K
task_categories:
- audio-classification
- automatic-speech-recognition
pretty_name: Skit-S2I
tags:
- intent-recognition
- speech
dataset_info:
  features:
  - name: audio
    dtype: audio
  - name: intent_class
    dtype: int64
  - name: template
    dtype: string
  - name: speaker_id
    dtype: int64
  splits:
  - name: train
    num_bytes: 698801842.48
    num_examples: 10445
  - name: test
    num_bytes: 93949690.4
    num_examples: 1400
  download_size: 495247674
  dataset_size: 792751532.88
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

Skit-S2I is a **Speech to Intent** dataset for Indian English (`en-IN`), that covers 14 coarse-grained intents from the Banking domain. This work is inspired by a similar release in the [Minds-14 dataset](https://huggingface.co/datasets/PolyAI/minds14) - here, we restrict ourselves to Indian English but with a larger training set. The dataset is split into:
- test - `100` samples per intent
- train - `>650` samples per intent

The data was generated by 11 Indian speakers, recording over a telephony line. We also provide access to anonymised speaker information - like gender, languages spoken, native language - to enable more structured discussions around robustness and bias, in the models you train.


<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
  <p>This Datasheet follows from the <a href="https://arxiv.org/pdf/1803.09010.pdf" target="_blank">Datasheets for datasets</a> paper.</p>
</div>

# Motivation

**Q1) For what purpose was the dataset created ? Was there a specific task in mind ? Was there a specific gap that needed to be filled ?**

Ans. This is a dataset for Intent classification from (Indian English) speech, and covers 14 coarse-grained intents from the Banking domain. While there are other datasets that have approached this task, here we provide a much largee training dataset (`>650` samples per intent) to train models in an end-to-end fashion. We also provide anonymised speaker information to help answer questions around model robustness and bias.

**Q2) Who created the dataset and on behalf of which entity ?**

Ans. The (internal) Operations team at Skit was involved in the generation of the dataset, and provided their information for (anonymous) release. [Unnati Senani](https://unnu.so/about/) was involved in the curation of utterance templates, and [Kriti Anandan](https://github.com/kritianandan98) and [Kumarmanas Nethil](https://huggingface.co/janaab) were involved in the planning and collection of utterances - using an internal tool called [sandbox](https://github.com/skit-ai/sandbox). These contributors worked on this dataset as part of the Conversational UX and ML teams at Skit.

**Q3) Who funded the creation of the dataset ?**

Ans. Skit funded the creation of this dataset.

# Composition

**Q4) What do the instances that comprise the dataset consist of ?**

Ans. The intent dataset is split across `train.csv` and `test.csv`. In both, individual instances consist of the following fields:
- `id`
- `intent_class`
- `template`
- `audio_path`
- `speaker_id`

You can trace more information on the intents, using the shared `intent_class` field in `intent_info.csv`:
- `intent_class`
- `intent_name`
- `description`

You can trace more information on the speakers, using the shared `speaker_id` field in `speaker_info.csv`:
- `speaker_id`
- `native_language`
- `languages_spoken`
- `places_lived`
- `gender`


**Q5) How many instances are there in total (of each type, if appropriate) ?**

Ans. In all there are `11845` samples, across the train and test splits:

- `test.csv` has a total of `1400` samples, with exactly `100` samples per intent
- `train.csv` has a total of `10445` samples, with atleast `650` samples per intent

The 11 speakers are distributed across the dataset, but unequally. However:
- each intent has data from all speakers 
- the speakers are stratified across the train and test split - for each intent independently

Some statistics on the speakers are provided below. More granular information can be found in `speaker_info.csv`:
- Native languages: `Hindi`(4), `Bengali`(3), `Kannada`(2), `Malayalam`(1), `Punjabi`(1)
- Languages spoken: `Hindi`, `English`, `Bengali`, `Odia`, `Kannada`, `Punjabi`, `Malayalam`, `Bihari`, `Marathi`
- Indian states lived in: `Bihar`, `Odisha`, `Karnataka`, `West Bengal`, `Punjab`, `Kerala`, `Jharkhand`, `Maharashtra`

**Q6) Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set ?**

Ans. For each intent, our Conversational UX team generated a list of templates. These are meant to be a (satisfactory) representation of all the variations in utterances, seen in human speech. These templates were used as a guide by the speakers when generating data. So, this dataset is limited by the templates and the variations that speakers added (spontaneously).

**Q7) Are there recommended data splits (e.g., training, development/validation, testing) ?**

Ans. The recommended split into train and test sets is provided as `train.csv` and `test.csv` respectively.

**Q8) Are there any errors, sources of noise, or redundancies in the dataset?**

Ans. There could be channel noise present in the dataset, because the data was generated through telephone calls. However, background noise will not be as prevalent as in real-world scenarios, since these telephone calls were made in a semi-controlled environment.

**Q9) Other comments.**

Ans. Speakers were responsible for generating variations in utterances, using the `template` field as a guide. So, there could be some unintentional overlap across the content of utterances.

# Collection Process

**Q10) How was the data associated with each instance acquired ?**

Ans. Members of the (internal) Operation team generated each utterance - using the associated `template` field as a guide, and injecting their own variations into the speech utterance.

**Q11) Who was involved in the data collection process and how were they compensated ?**

Ans. The data was generated by the (internal) Operations team and they are/were full-time employees.

**Q12) Over what timeframe was the data collected ?**

Ans. This data was collected over a time period of 1 month.

**Q13) Was any preprocessing/cleaning/labelling of the data done ?**

Ans. Audio instances in the dataset were *auto-labelled* with their associated `intent` and `template` fields. For more information on this, refer to the documentation of [sandbox](https://github.com/skit-ai/sandbox).

# Recommended Uses

**Q14) Has the dataset been used for any tasks already ?**

Ans. It has been used to benchmark models for the task of intent classification from speech.

**Q15) What (other) tasks could the dataset be used for ?**

Ans. We provide speaker characteristics. So, this dataset could be used for alternate classification tasks from speech - like, gender or native language.

# Distribution and Maintenance

**Q16) Will the dataset be distributed under a copyright or other intellectual property (IP) license ?**

Ans. This dataset is being distributed under a [CC BY NC license](https://creativecommons.org/licenses/by-nc/4.0/).

**Q17) Who will be maintaining the dataset ?**

Ans. The research team at Skit will be maintaining the dataset. They can be contacted by sending an email to ml-research@skit.ai.

**Q18) Will the dataset be updated in the future (e.g., to correct labelling errors, add new instances, delete instances) ?**

Ans. Incase there are errors, we will try to collate and share an updated version every 3 months. We also plan to add more instances and variations to the dataset - to make it more robust.