# With One Voice: Composing a Travel Voice Assistant from Re-purposed Models

arXiv: https://arxiv.org/abs/2108.11463 · HTML: https://mlsanity.com/paper/2108.11463/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 4 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [Streaming End-to-end Speech Recognition For Mobile Devices](https://mlsanity.com/paper/1811.06621/) (2018)
- [Applying Deep Learning To Airbnb Search](https://mlsanity.com/paper/1810.09591/) (2018)
- [Convo: What does conversational programming need? An exploration of machine learning interface design](https://mlsanity.com/paper/2003.01318/) (2020)
- [Machine Translation at Booking.com: Journey and Lessons Learned](https://mlsanity.com/paper/1707.07911/) (2017)

## Built on by (most cited first, 2 of 2)

- [Talk2Care: Facilitating Asynchronous Patient-Provider Communication with Large-Language-Model](https://mlsanity.com/paper/2309.09357/) (2023)
- [Maturity Framework for Enhancing Machine Learning Quality](https://mlsanity.com/paper/2502.15758/) (2025)

## Abstract (arXiv)

Voice assistants provide users a new way of interacting with digital products, allowing them to retrieve information and complete tasks with an increased sense of control and flexibility. Such products are comprised of several machine learning models, like Speech-to-Text transcription, Named Entity Recognition and Resolution, and Text Classification. Building a voice assistant from scratch takes the prolonged efforts of several teams constructing numerous models and orchestrating between components. Alternatives such as using third-party vendors or re-purposing existing models may be considered to shorten time-to-market and development costs. However, each option has its benefits and drawbacks. We present key insights from building a voice search assistant for Booking.com search and recommendation system. Our paper compares the achieved performance and development efforts in dedicated tailor-made solutions against existing re-purposed models. We share and discuss our data-driven decisions about implementation trade-offs and their estimated outcomes in hindsight, showing that a fully functional machine learning product can be built from existing models.