Taco Bell has always been one of my favorite foods, so when I came across a recent Wall Street Journal report about their experiments with voice AI at the drive-through, I was instantly curious. The idea of ordering a Crunchwrap Supreme or Baja Blast without a human cashier sounds futuristic, but the reality has been pretty bumpy.

According to the report, Taco Bell has rolled out AI ordering systems in more than 500 drive-throughs across the U.S. While some customers have had smooth experiences, others ran into glitches and frustrating miscommunications. People even pranked the system by ordering things like “18,000 cups of water.” Because of this, Taco Bell is rethinking how it uses AI. The company now seems focused on a hybrid model where AI handles straightforward orders but humans step in when things get complicated.

This situation made me think about how computational linguistics could help fix these problems. Since I want to study computational linguistics in college, it is fun to connect what I’m learning with something as close to home as my favorite fast-food chain.

---

Where Computational Linguistics Can Help

1. Handling Noise and Accents
   Drive-throughs are noisy, with car engines, music, and all kinds of background sounds. Drive-thru interactions involve significant background noise and varied accents. Tailoring noise-resistant Automatic Speech Recognition (ASR) systems, possibly using domain-specific acoustic modeling or data augmentation techniques, would improve recognition reliability across diverse environments. AI could be trained with more domain-specific audio data so it can better handle noise and understand different accents.
2. Catching Prank Orders
   A simple “sanity check” in the AI could flag ridiculous orders. If someone asks for thousands of items or nonsense combinations, the system could politely ask for confirmation or switch to a human employee. Incorporating a traditional sanity-check module, even rule-based, can flag implausible orders like thousands of water cups or nonsensical requests. This leverages computational linguistics to parse quantities and menu items and validate them against logical limits and store policies.
3. Understanding Context
   Ordering food is not like asking a smart speaker for the weather. People use slang, pause, or change their minds mid-sentence. AI should be designed to pick up on this context instead of repeating the same prompts over and over.
4. Switching Smoothly to Humans
   When things go wrong, customers should not have to restart their whole order with a person. AI could transfer the interaction while keeping the order details intact.
5. Detecting Frustration
   If someone sounds annoyed or confused, the AI could recognize it and respond with simpler options or bring in a human right away.

---

Why This Matters

The point of voice AI is not just to be futuristic. It is about making the ordering process easier and faster. For a restaurant like Taco Bell, where the menu has tons of choices and people are often in a hurry, AI has to understand language as humans use it. Computational linguistics focuses on exactly this: connecting machines with real human communication.

I think Taco Bell’s decision to step back and reassess is actually smart. Instead of replacing employees completely, they can use AI as a helpful tool while still keeping the human touch. Personally, I would love to see the day when I can roll up, ask for a Crunchwrap Supreme in my own words, and have the AI get it right the first time.

---

Further Reading

• Cui, Wenqian, et al. “Recent Advances in Speech Language Models: A Survey.” Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, 2025, pp. 13943–13970. ACL Anthology
• Zheng, Xianrui, Chao Zhang, and Philip C. Woodland. “DNCASR: End-to-End Training for Speaker-Attributed ASR.” Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, 2025, pp. 18369–18383. ACL Anthology
• Imai, Saki, Tahiya Chowdhury, and Amanda J. Stent. “Evaluating Open-Source ASR Systems: Performance Across Diverse Audio Conditions and Error Correction Methods.” Proceedings of the 31st International Conference on Computational Linguistics (COLING 2025), 2025, pp. 5027–5039. ACL Anthology
• Hopton, Zachary, and Eleanor Chodroff. “The Impact of Dialect Variation on Robust Automatic Speech Recognition for Catalan.” Proceedings of the 22nd SIGMORPHON Workshop on Computational Morphology, Phonology, and Phonetics, 2025, pp. 23–33. ACL Anthology
• Arora, Siddhant, et al. “On the Evaluation of Speech Foundation Models for Spoken Language Understanding.” Findings of the Association for Computational Linguistics: ACL 2024, 2024, pp. 11923–11938. ACL Anthology
• Cheng, Xuxin, et al. “MoE-SLU: Towards ASR-Robust Spoken Language Understanding via Mixture-of-Experts.” Findings of the Association for Computational Linguistics: ACL 2024, 2024, pp. 14868–14879. ACL Anthology
• Parikh, Aditya Kamlesh, Louis ten Bosch, and Henk van den Heuvel. “Ensembles of Hybrid and End-to-End Speech Recognition.” Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), 2024, pp. 6199–6205. ACL Anthology
• Mujtaba, Dena, et al. “Lost in Transcription: Identifying and Quantifying the Accuracy Biases of Automatic Speech Recognition Systems Against Disfluent Speech.” Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024, pp. 4795–4809. ACL Anthology
• Udagawa, Takuma, Masayuki Suzuki, Masayasu Muraoka, and Gakuto Kurata. “Robust ASR Error Correction with Conservative Data Filtering.” Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, 2024, pp. 256–266. ACL Anthology

— Andrew