# Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers

arXiv: https://arxiv.org/abs/2104.01604 · HTML: https://mlsanity.com/paper/2104.01604/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 43 references that are in the map.
- First posted on arXiv 4 Apr 2021.

## Builds on (most cited first, 24 of 24)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis](https://mlsanity.com/paper/1806.04558/) (2018)
- [Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning](https://mlsanity.com/paper/1609.06773/) (2016)
- [SLURP: A Spoken Language Understanding Resource Package](https://mlsanity.com/paper/2011.13205/) (2020)
- [Speech Model Pre-training for End-to-End Spoken Language Understanding](https://mlsanity.com/paper/1904.03670/) (2019)
- [An analysis of incorporating an external language model into a sequence-to-sequence model](https://mlsanity.com/paper/1712.01996/) (2017)
- [Towards end-to-end spoken language understanding](https://mlsanity.com/paper/1802.08395/) (2018)
- [Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks](https://mlsanity.com/paper/1904.03416/) (2019)
- [Rethinking Evaluation in ASR: Are Our Models Robust Enough?](https://mlsanity.com/paper/2010.11745/) (2020)
- [From Audio to Semantics: Approaches to end-to-end spoken language understanding](https://mlsanity.com/paper/1809.09190/) (2018)
- [Viewmaker Networks: Learning Views for Unsupervised Representation Learning](https://mlsanity.com/paper/2010.07432/) (2020)
- [Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems](https://mlsanity.com/paper/2010.04284/) (2020)
- [Spoken Language Understanding on the Edge](https://mlsanity.com/paper/1810.12735/) (2018)
- [Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models](https://mlsanity.com/paper/1910.09463/) (2019)
- [Reducing malicious use of synthetic media research: Considerations and potential release practices for machine learning](https://mlsanity.com/paper/1907.11274/) (2019)
- [Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification](https://mlsanity.com/paper/2102.07370/) (2021)
- [SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding](https://mlsanity.com/paper/2010.02295/) (2020)
- [A low latency ASR-free end to end spoken language understanding system](https://mlsanity.com/paper/2011.04884/) (2020)

## Built on by (most cited first, 11 of 11)

- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding](https://mlsanity.com/paper/2111.02735/) (2021)
- [SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Tasks](https://mlsanity.com/paper/2212.10525/) (2022)
- [Towards Spoken Mathematical Reasoning: Benchmarking Speech-based Models over Multi-faceted Math Problems](https://mlsanity.com/paper/2505.15000/) (2025)
- [Match to Win: Analysing Sequences Lengths for Efficient Self-supervised Learning in Speech and Audio](https://mlsanity.com/paper/2209.15575/) (2022)
- [Finstreder: Simple and fast Spoken Language Understanding with Finite State Transducers using modern Speech-to-Text models](https://mlsanity.com/paper/2206.14589/) (2022)
- [MSNER: A Multilingual Speech Dataset for Named Entity Recognition](https://mlsanity.com/paper/2405.11519/) (2024)
- [Digits micro-model for accurate and secure transactions](https://mlsanity.com/paper/2402.01931/) (2024)
- [Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language Understanding](https://mlsanity.com/paper/2405.15097/) (2024)
- ["KAN you hear me?" Exploring Kolmogorov-Arnold Networks for Spoken Language Understanding](https://mlsanity.com/paper/2505.20176/) (2025)
- [SLURP-TN : Resource for Tunisian Dialect Spoken Language Understanding](https://mlsanity.com/paper/2603.21940/) (2026)

## Abstract (arXiv)

This paper introduces Timers and Such, a new open source dataset of spoken English commands for common voice control use cases involving numbers. We describe the gap in existing spoken language understanding datasets that Timers and Such fills, the design and creation of the dataset, and experiments with a number of ASR-based and end-to-end baseline models, the code for which has been made available as part of the SpeechBrain toolkit.