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Evaluate a multilingual voice agent across the whole interaction: what it heard, what it understood, which critical details it preserved, what action it took, and what it said back. Word error rate (WER) alone cannot show whether the agent completed the request. Report task accuracy separately from spoken-interaction quality, and break results down by language, code-switch pattern, accent, and acoustic condition.
What should count as a successful voice-agent interaction?
Define success before running tests. For each scenario, write down the required end state, applicable policy constraints, and every field that must be correct for the task to count as a pass. A plausible-sounding response is not a pass if the agent used the wrong amount, changed an identifier, or failed to perform the requested action.
Specify the task and its critical fields
For a booking, support request, or transaction, list the fields that determine execution: for example, a date, amount, account identifier, destination, or stated constraint. Decide in advance whether each field must match exactly, may be normalized (such as a spoken date rendered in a standard format), or can be omitted. Then define the verified task state—for instance, whether a record was actually created or updated—not just the response the agent produced.
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Separate accuracy from experience
Keep two scorecards. EVA-Bench, published by ServiceNow AI Research in October 2026, distinguishes accuracy dimensions such as task completion, faithfulness, and speech fidelity (EVA-A) from experience dimensions such as conversation progression, conciseness, and turn timing (EVA-X). An agent can complete a task while interrupting repeatedly, or sound natural while failing to carry it out; one score should not hide that difference.
How should the test set represent multilingual speech?
Choose languages and switching patterns based on the people and tasks the agent will actually serve. A benchmark’s language coverage is evidence about that benchmark, not a universal list of what a deployed system can handle.
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Include the ways speakers actually switch
Where relevant, test switches within a sentence as well as longer stretches in another language. Include different speakers and accents, and make sure the examples cover task-critical words in both languages—for instance, a request spoken mostly in one language with a name, date, or address in another. A test set that contains bilingual utterances but no critical details near a language boundary may miss the errors that matter most.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsUse native-speaker review where feasible to check that prompts and reference transcripts are natural and that language-specific scoring choices are defensible. τ-Multilingual reports native-speaker review; the ServiceNow code-switch ASR benchmark describes review by an AI/NLP linguist who is a native speaker of the matrix language.
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Vary acoustic conditions deliberately
Test realistic noise and a range of accents rather than assuming clean, studio-like audio represents customers. τ-Voice evaluates realistic audio, including noise and diverse accents, while EVA-Bench includes controlled accent and noise perturbations. Record the condition for every result so a clean-audio score is not mistaken for performance under a noisy call or unfamiliar accent.
Use published benchmarks as coverage examples, not a leaderboard
| Work | Reported scope | What it can inform |
|---|---|---|
| τ-Multilingual (2026) | 4,500 full-duplex calls; Spanish, Portuguese, Hindi, Korean, and Mandarin | Language-specific task and interaction outcomes; its reported results should stay attached to the benchmark and configuration. |
| τ-Voice (2026) | 278 tasks | Grounded-task completion and robustness testing with realistic audio conditions. |
| EVA-Bench (ServiceNow AI Research, October 2026) | 213 scenarios across three enterprise domains | Separate accuracy and experience dimensions, including repeated-run measures. |
| ServiceNow code-switch ASR benchmark (2026) | 259 Spanish-English, 298 French-English, 188 Canadian French-English, and 173 German-English records | Code-switch recognition measures across four named language pairings; the article describes synthesized audio, not spontaneous natural speech. |
| HiKE (2026) | Korean-English; non-synthetic speech | An example of code-switch evaluation using non-synthetic speech for a specific language pair. |
These works differ in language coverage, tasks, audio, and system configuration, so their scores are not a direct cross-paper ranking. For example, τ-Multilingual reports distinct outcomes across its five languages, but that does not establish a universal ranking of languages or predict performance on a different system.
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Which metrics reveal where the agent failed?
Score the pipeline in layers. Each measure answers a different question, and none should stand in for the whole interaction.
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Transcription and meaning
- WER: measures word-level transcription errors. Break it down by language when possible, but do not treat every word error as equally consequential.
- Semantic preservation: check whether the intended meaning survived, especially around code-switches. The ServiceNow benchmark reports semantic word error rate (SWER) as well as WER.
- Answer error: measure whether the task-relevant answer is wrong. The ServiceNow benchmark reports answer error rate alongside WER and SWER, illustrating that lexical, semantic, and answer-level errors are distinct.
Critical fields, action, and policy
- Critical-field accuracy: score required values independently, including entity recognition and normalization. Make the scoring rule explicit for each field.
- Task completion: verify the result in the target environment or with a task grader. τ-Voice uses verifiable completion of grounded tasks; EVA-Bench includes task completion.
- Faithfulness: check whether the agent followed the request and applicable policy rather than merely returning a correct-looking answer. EVA-Bench includes faithfulness as an accuracy dimension.
- Response completeness: compare the spoken response with the information the scenario requires. Microsoft’s Voice Live guidance includes response-completeness and task-completion evaluators; it notes that the described evaluation does not measure WER or mean opinion score (MOS).
Spoken interaction
Score turn-taking, interruptions, conversation progression, conciseness, timing, and speech fidelity separately from whether the task succeeded. These measures help distinguish an accurate but frustrating interaction from a smooth conversation that loses important information.
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How can you test reliability rather than a lucky run?
Repeat scenarios when model behavior or simulated dialogue is stochastic. Report the number of runs and the sampling protocol, then distinguish a single-run result from performance across repeated attempts. EVA-Bench reports pass@1, pass@k, and pass^k to distinguish peak performance from reliable capability; preserve the benchmark’s definitions when using those measures rather than treating them as interchangeable.
For your own evaluation, state what counts as passing each run and how repeated outcomes are summarized. A single successful interaction demonstrates that the agent can succeed under that test condition; it does not establish that it will do so consistently.
How should failures be diagnosed and reported?
For each failed scenario, identify the earliest stage where the result diverged from the expected path. A practical diagnosis map is:
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- Speech recognition: Were the words or language segments transcribed incorrectly?
- Language identification and switching: Did the system miss a language change or assign a span to the wrong language?
- Entity handling: Was a name, date, amount, identifier, or constraint lost or normalized incorrectly?
- Dialogue state and policy: Did the agent misunderstand what remained to be clarified, or fail to follow a required constraint?
- Tool choice and execution: Did it select the wrong action, or fail to produce the intended state change?
- Response generation: Did the spoken response omit required information, misstate the outcome, or create an interaction problem?
This is a useful diagnostic taxonomy, not a mandatory standard established by the cited benchmarks. In the report, break results down by language, language pair, switching pattern, accent, and noise condition. Keep separate figures for transcription, meaning, critical fields, verified task completion, interaction, and repeated-run reliability instead of compressing them into one headline score.
What can user preference research tell you about code-mixing?
Whether an agent should mirror a speaker’s code-mixing is partly a user-experience question, not just a recognition question. A 2020 Microsoft Research study by Bawa, Khadpe, Joshi, Bali, and Choudhury used 91 bilingual participants to study perceptions of chatbots that reciprocate users’ code-mixing. That study concerned text chatbots; its findings should not be treated as direct evidence about spoken voice agents. If mirroring language choice matters to the product, test it with the intended users and measure preference separately from task accuracy.
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