# TorchAudio-Squim: Reference-less Speech Quality and Intelligibility measures in TorchAudio

arXiv: https://arxiv.org/abs/2304.01448 · HTML: https://mlsanity.com/paper/2304.01448/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2023.

## Built on by (most cited first, 30 of 59)

- [Audiobox: Unified Audio Generation with Natural Language Prompts](https://mlsanity.com/paper/2312.15821/) (2023)
- [Natural language guidance of high-fidelity text-to-speech with synthetic annotations](https://mlsanity.com/paper/2402.01912/) (2024)
- [SALM-Duplex: Efficient and Direct Duplex Modeling for Speech-to-Speech Language Model](https://mlsanity.com/paper/2505.15670/) (2025)
- [Koel-TTS: Enhancing LLM based Speech Generation with Preference Alignment and Classifier Free Guidance](https://mlsanity.com/paper/2502.05236/) (2025)
- [Differentiable Reward Optimization for LLM based TTS system](https://mlsanity.com/paper/2507.05911/) (2025)
- [NanoCodec: Towards High-Quality Ultra Fast Speech LLM Inference](https://mlsanity.com/paper/2508.05835/) (2025)
- [SpeechPrompt: Prompting Speech Language Models for Speech Processing Tasks](https://mlsanity.com/paper/2408.13040/) (2024)
- [Low Frame-rate Speech Codec: a Codec Designed for Fast High-quality Speech LLM Training and Inference](https://mlsanity.com/paper/2409.12117/) (2024)
- [CapSpeech: Enabling Downstream Applications in Style-Captioned Text-to-Speech](https://mlsanity.com/paper/2506.02863/) (2025)
- [TTSDS2: Resources and Benchmark for Evaluating Human-Quality Text to Speech Systems](https://mlsanity.com/paper/2506.19441/) (2025)
- [Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech](https://mlsanity.com/paper/2402.16321/) (2024)
- [Rethinking MUSHRA: Addressing Modern Challenges in Text-to-Speech Evaluation](https://mlsanity.com/paper/2411.12719/) (2024)
- [Discrete Diffusion for Generative Modeling of Text-Aligned Speech Tokens](https://mlsanity.com/paper/2509.20060/) (2025)
- [AV2Wav: Diffusion-Based Re-synthesis from Continuous Self-supervised Features for Audio-Visual Speech Enhancement](https://mlsanity.com/paper/2309.08030/) (2023)
- [Vibravox: A Dataset of French Speech Captured with Body-conduction Audio Sensors](https://mlsanity.com/paper/2407.11828/) (2024)
- [Arab Voices: Mapping Standard and Dialectal Arabic Speech Technology](https://mlsanity.com/paper/2601.13319/) (2026)
- [NemotronLabs VoiceChat: An Open Full-duplex Speech-to-Speech Model with Tool Calling Capabilities](https://mlsanity.com/paper/2609.21967/) (2026)
- [AnCoGen: Analysis, Control and Generation of Speech with a Masked Autoencoder](https://mlsanity.com/paper/2501.05332/) (2025)
- [MB-RIRs: a Synthetic Room Impulse Response Dataset with Frequency-Dependent Absorption Coefficients](https://mlsanity.com/paper/2507.09750/) (2025)
- [DuplexChat: Constructing Speaker-Separated Full-Duplex Dialogue Speech at Scale for Spoken Dialogue Language Modeling](https://mlsanity.com/paper/2607.04941/) (2026)
- [VoiceChat-TTS: A Low-Latency Continuous Speech Synthesis Model for Interactive Agents](https://mlsanity.com/paper/2608.13831/) (2026)
- [Resource-Efficient Speech Quality Prediction through Quantization Aware Training and Binary Activation Maps](https://mlsanity.com/paper/2407.04578/) (2024)
- [Direction of Arrival Correction through Speech Quality Feedback](https://mlsanity.com/paper/2408.07234/) (2024)
- [C2AV-TSE: Context and Confidence-aware Audio Visual Target Speaker Extraction](https://mlsanity.com/paper/2504.00750/) (2025)
- [WhiSQA: Non-Intrusive Speech Quality Prediction Using Whisper Encoder Features](https://mlsanity.com/paper/2508.02210/) (2025)
- [OMHBench: Benchmarking Balanced and Grounded Omni-Modal Multi-Hop Reasoning](https://mlsanity.com/paper/2508.16198/) (2025)
- [NaturalVoices: A Large-Scale, Spontaneous and Emotional Podcast Dataset for Voice Conversion](https://mlsanity.com/paper/2511.00256/) (2025)
- [From Diet to Free Lunch: Estimating Auxiliary Signal Properties using Dynamic Pruning Masks in Speech Enhancement Networks](https://mlsanity.com/paper/2602.10666/) (2026)
- [The Limits of Reference-Free Speech Quality Metrics as Evaluators and Rewards on Modern Text-to-Speech](https://mlsanity.com/paper/2609.13150/) (2026)
- [Fast Streaming Transducer ASR Prototyping via Knowledge Distillation with Whisper](https://mlsanity.com/paper/2409.13499/) (2024)

## Abstract (arXiv)

Measuring quality and intelligibility of a speech signal is usually a critical step in development of speech processing systems. To enable this, a variety of metrics to measure quality and intelligibility under different assumptions have been developed. Through this paper, we introduce tools and a set of models to estimate such known metrics using deep neural networks. These models are made available in the well-established TorchAudio library, the core audio and speech processing library within the PyTorch deep learning framework. We refer to it as TorchAudio-Squim, TorchAudio-Speech QUality and Intelligibility Measures. More specifically, in the current version of TorchAudio-squim, we establish and release models for estimating PESQ, STOI and SI-SDR among objective metrics and MOS among subjective metrics. We develop a novel approach for objective metric estimation and use a recently developed approach for subjective metric estimation. These models operate in a ``reference-less" manner, that is they do not require the corresponding clean speech as reference for speech assessment. Given the unavailability of clean speech and the effortful process of subjective evaluation in real-world situations, such easy-to-use tools would greatly benefit speech processing research and development.