ASR and TTS Troubleshooting

This document covers the most common speech recognition (ASR) and text-to-speech (TTS) issues encountered in Voice Gateway deployments. Each section is structured as: Symptom > Root Cause > Fix, with parameter reference tables and known limitations noted where applicable. All runtime parameters are set using:

userSessionUtils.setCallControlParam('paramName', value);

Microsoft Azure ASR

Utterance Splitting — Speech Split into Multiple Parts

Symptom A continuous user utterance arrives as multiple separate inputs. For example, Hello, I want to check my balance is received as two turns: Hello then I want to check my balance. This causes intent mismatches, loop behavior, or responses to partial input. Root Cause Azure Speech uses AzureSegmentationSilenceTimeoutMs to detect end-of-speech. Natural pauses of 300 ms or more between words can trigger a premature commit. The default value is too aggressive for conversational IVR use cases. Fix Add a Script node at the start of your Experience Flow:

// Standard conversational IVR
userSessionUtils.setCallControlParam(
  'AzureSegmentationSilenceTimeoutMs', 1200
);

// Longer conversational utterances (Agentic app flows)
userSessionUtils.setCallControlParam(
  'AzureSegmentationSilenceTimeoutMs', 2000
);

// Do NOT exceed 3000 — increases latency significantly.

Start with 1200 ms and increase in 200 ms increments if splitting persists. Test with real callers in your target language before going live.

Parameter Reference

Parameter	Range	Purpose
`AzureSegmentationSilenceTimeoutMs`	800-2000 ms	End-of-speech silence detection. Lower = faster response. Higher = fewer splits.
`sttMinConfidence`	0.4-0.6	Reduce to accept more valid inputs; raise to filter background noise.
`botNoInputTimeoutMS`	5000-8000 ms	Increase for non-English speakers or callers who pause before speaking.

For more details, refer to Speech Customization

Digit and Number Misinterpretation

Symptom Numbers are misinterpreted by the ASR engine. Common patterns include:

Spoken Hindi numbers such as ek combined with 22,000 becoming 1,202,000.
Card numbers with repeated digits being truncated or garbled.
Confidence scores above threshold (for example, 75.7%) but transcription is incorrect.

Fix

// Enable continuous ASR at the digit-collection entity node
userSessionUtils.setCallControlParam('continuousASR', true);
userSessionUtils.setCallControlParam('continuousASRTimeoutInMS', 2000);

// DTMF fallback — strongly recommended for 16-digit card numbers
userSessionUtils.setCallControlParam('dtmfCollectMaxDigits', 16);
userSessionUtils.setCallControlParam('dtmfCollectInterDigitTimeoutMS', 4000);

Input Type Recommendations

Input Type	Recommended Method	Configuration Notes	Error Rate
4-8 digit PIN	Speech ASR	Enable `continuousASR`	Low
10-digit phone number	Speech or DTMF	Speech for English; DTMF for non-English	Medium
16-digit card number	DTMF preferred	ASR struggles with repeated digits-use DTMF as primary	High with ASR
Currency amounts (Hindi)	Speech + lower threshold	`sttMinConfidence=0.4` + `AzureSegmentationSilenceTimeoutMs=1500`	Medium

Widespread ASR Failure-Outage or Concurrency Breach

Symptom All IVR calls fail simultaneously. The bot responds with the default error message and no speech is recognized across any bots. An Azure region outage typically causes a concurrency limit breach or expired API credentials. Immediate Recovery Steps

Verify the affected Azure region at Azure Status. Confirm whether the issue is a full outage or a concurrency limit breach.
Switch active calls to the fallback ASR label configured in your Voice Gateway settings.
If the issue is caused by expired API credentials, update the Azure Speech key in the platform channel configuration and save the changes.
Place a test call and confirm that speech recognition resumes before you re-enable production traffic.

Prevention — Configure Before Go-Live

Primary ASR:  Label = your-primary-label    (primary region)
Fallback ASR: Label = your-fallback-label   (different region, same vendor)

Primary and Fallback must use different labels. Using the same label for both provides no fallback protection.

Always configure fallback ASR/TTS before going to production. Use the same vendor but a different region for optimal compatibility. For vendor-level fallback, configure a secondary vendor (for example, Deepgram as a fallback for Azure).

Microsoft Azure TTS

Mispronounced Words-SSML Phoneme and Say-As Tags

Symptom Azure Neural Voice mispronounces product names, currency amounts, acronyms, or non-English terms. Digits may be read as words — for example, 6987 spoken as Che Nau Sath Aath Feet when using a mismatched voice. Fix Use SSML tags in your Message nodes:

<!-- Currency amounts -->
<speak>
  <say-as interpret-as="currency" language="hi-IN">
    ₹3,40,650.23
  </say-as>
</speak>

<!-- Digit-by-digit (prevents misread artifacts) -->
<speak>
  Your OTP is <say-as interpret-as="digits">6987</say-as>
</speak>

<!-- Acronym spelled out -->
<speak>
  <say-as interpret-as="spell-out">EMI</say-as>
</speak>

<!-- Pause before key information -->
<speak>
  Your balance is <break time="300ms"/>
  <say-as interpret-as="currency">₹5000</say-as>
</speak>

SSML Tag Reference

Problem	SSML Fix	Effect
Currency read incorrectly	`<say-as interpret-as="currency">`	Reads with correct language context
Digits read as words	`<say-as interpret-as="digits">`	Forces digit-by-digit: `six nine eight seven`
Acronym mispronounced	`<say-as interpret-as="spell-out">`	Spells out letter by letter: `E-M-I`
Brand name incorrect	`<sub alias="correct pronunciation">`	Replaces text with specified pronunciation
Need a pause	`<break time="500ms"/>`	Inserts a 0.5-second pause
Speech too fast	`<prosody rate="slow">`	Reduces speech rate for complex information

TTS Reading Newlines from LLM or Agentic Responses

Symptom When using LLM or Agentic app responses in voice flows, the TTS engine reads literal newline characters aloud — saying 'backslash n backslash n' instead of pausing. This occurs because LLM outputs contain formatting characters (\n, markdown bold, bullet points) that TTS can’t interpret. Fix Add a Script node before the Message node to sanitize the LLM output:

// Replace 'YourLLMNode' with your actual node name
var raw = context.steps.YourLLMNode.response || '';

var clean = raw
  .replace(/\\n/g, ' ')        // literal \n strings from LLM
  .replace(/\n/g, ' ')          // actual newline characters
  .replace(/\r/g, ' ')          // carriage returns
  .replace(/\*\*/g, '')         // markdown bold
  .replace(/\*/g, '')           // markdown bullets/italic
  .replace(/#+\s/g, '')         // markdown headers
  .replace(/^\s*[-•]\s*/gm, '') // bullet characters
  .replace(/\s{2,}/g, ' ')      // collapse multiple spaces
  .trim();

context.ttsOutput = clean;
// Use context.ttsOutput in your Message node

There is no call control parameter for this fix. Sanitization must be applied in a Script node before the Message node. Adjust the variable path (context.steps.YourLLMNode.response) to match your specific flow design.

Hindi TTS Failure-Voice-Language Mismatch

Symptom On mid-call language switch to Hindi, TTS throws a 'synthAudio requires language' error. Logs show 'language: undefined' and 'voice: undefined'. Digits may be spoken incorrectly when an English voice receives Devanagari numerals. Root Cause The voice name and language code don’t match. For example, using language=hi-IN with voice=en-IN-AartiIndicNeural (an English-Indian voice, not a Hindi voice). Fix Add a Script node before the first Hindi message node:

userSessionUtils.setCallControlParam('ttsLanguage', 'hi-IN');
userSessionUtils.setCallControlParam('voiceName', 'hi-IN-SwaraNeural');
userSessionUtils.setCallControlParam('ttsProvider', 'microsoft');

// WRONG:   language=hi-IN + voice=en-IN-AartiIndicNeural
// CORRECT: language=hi-IN + voice=hi-IN-SwaraNeural

Supported Hindi Voices

Voice Name	Gender	Language	Notes
`hi-IN-SwaraNeural`	Female	`hi-IN`	Recommended natural Hindi.
`hi-IN-MadhurNeural`	Male	`hi-IN`	Alternate male voice.
`en-IN-AartiIndicNeural`	Female	`en-IN`	English-Indian (don’t use for Hindi text).

The system doesn’t validate voice-language matching during configuration. Manually verify that the voiceName language prefix matches the ttsLanguage code.

Configured Voice Not Applying

Symptom The voice configured in Voice Preferences (for example, hi-IN-SwaraNeural) isn’t applied during live calls. The bot uses the default English voice instead, and no error displays. Root Cause TTS initializes at the first message node. If automation or a script runs first, TTS uses defaults before the voice configuration loads. Resolution Options

Option	Action
Fix 1 (Recommended)	Add a short Message node as the first node in your Flow-before any script or automation node.
Fix 2	After saving voice config in the UI, edit the flow description field and re-save to force re-registration.
Fix 3 (Script)	Set voice via script: `userSessionUtils.setCallControlParam('voiceName', 'hi-IN-SwaraNeural');` before the message node.
Fix 4 (Google)	If you see `'No credentials for Google with labels: undefined'` — set `ttsLabel` using a script, not the UI. See No Credentials for Google.

Deepgram ASR / TTS

Utterance Splitting — utteranceEndMs Tuning

Symptom A user says a long sentence, but Deepgram commits mid-sentence due to natural pauses. Setting deepgramUtteranceEndMs to 3000 or 4000 ms results in splits. Root Cause deepgramUtteranceEndMs alone is insufficient. It must be paired with deepgramEndpointing to handle micro-pauses within sentences. The endpointing parameter controls Voice Activity Detection (VAD) sensitivity for short gaps; utteranceEndMs controls the final silence window. Fix

// Recommended for Agentic/Conversational flows
userSessionUtils.setCallControlParam('deepgramUtteranceEndMs', 1500);
userSessionUtils.setCallControlParam('deepgramEndpointing', 500);
userSessionUtils.setCallControlParam('sttMinConfidence', 0.4);
userSessionUtils.setCallControlParam('deepgramNumerals', true);
userSessionUtils.setCallControlParam('deepgramPunctuate', true);

// For Spanish / Latin American Spanish
userSessionUtils.setCallControlParam('sttLanguage', 'es-419');

Why smaller utteranceEndMs works better: endpointing=500 handles micro-pauses within speech. utteranceEndMs=1500 handles the longer gap at the end. Setting utteranceEndMs=3000-4000 delays the final commit but doesn’t fix mid-sentence splits.

Parameter Reference

Parameter	Range	Purpose
`deepgramUtteranceEndMs`	800-2000 ms	End-of-utterance silence window. Start at 1200; increase by 200 ms if splitting persists.
`deepgramEndpointing`	300-600 ms	VAD sensitivity. 300 ms = responsive; 600 ms = more patient with pauses.
`deepgramNumerals`	`true/false`	Converts spoken numbers to digits (for example, `five hundred` > `500`).
`deepgramNer`	`true/false`	Named Entity Recognition — improves proper noun and entity recognition.
`deepgramPunctuate`	`true/false`	Adds punctuation to transcripts-useful for NLU processing.
`deepgramKeyterms`	String array	Boosts recognition of specific listed words.

Digit and RX Number Recognition

Symptom The bot is collecting multi-digit numbers (for example, 5-7 digit prescription or account numbers). Callers pause between digits. The bot splits the utterance or misses digits. Production-Tested Configuration

var recognizerConfig = {
  vendor: 'deepgram',
  model: 'nova-3-medical', // Use nova-3 for non-medical
  language: 'en-US',
  interim: true,
  deepgramOptions: {
    endpointing: 300,
    utteranceEndMs: 1000,
    keyterms: [
      'zero', 'one', 'two', 'three', 'four',
      'five', 'six', 'seven', 'eight', 'nine'
    ],
    numerals: true
  }
};

userSessionUtils.setCallControlParam('dtmfBargein', false);
userSessionUtils.setCallControlParam('listenDuringPrompt', true);

Model Selection

Model	Best For	Use Case	Status
`nova-3`	Best general accuracy	Default IVR flows	Recommended
`nova-3-medical`	Medical/clinical terms, RX numbers	Healthcare, pharmacy, insurance	Recommended
`nova-2-phonecall`	Optimized for telephony audio	Call centre deployments	Stable
`enhanced`	High accuracy, slightly higher latency	Accuracy-critical flows	Stable

Google ASR & TTS

16-Digit Card Number — Repeated Digit Failure

Symptom Google ASR fails on sequences with 8 or more consecutive identical digits (for example, 509099999999990002). The bot may barge in early. This is a known Google ASR limitation. Fix

// Recommended: DTMF input for card numbers
userSessionUtils.setCallControlParam('dtmfCollectMaxDigits', 16);
userSessionUtils.setCallControlParam('dtmfCollectInterDigitTimeoutMS', 4000);
userSessionUtils.setCallControlParam('dtmfCollectTermDigit', '#');

// If speech must be used — enable continuous ASR
userSessionUtils.setCallControlParam('continuousASR', true);
userSessionUtils.setCallControlParam('continuousASRTimeoutInMS', 2500);

// Note: AzureSegmentationSilenceTimeoutMs does NOT affect Google ASR.

Model Selection

Model	Description	Use Case	Status
`chirp_2`	Best for telephony + accents	Recommended for IVR	Recommended
`chirp_telephony`	Optimized for 8 kHz/16 kHz audio	Telephony deployments	Recommended
`telephony`	Legacy — stable, widely tested	Default	Good default
`telephony_short`	Fast, for short utterances (less than 5 seconds)	Menu / yes-no commands	Recommended
`long`	Batch file processing	—	Don’t use for real-time IVR

No Credentials for Google

Symptom TTS fails with: 'No text-to-speech service credentials for Google with labels: undefined'. This occurs even after setting ttsLabel in the Voice Preferences. Root Cause The ttsLabel configured in the Voice Preferences UI doesn’t propagate to the TTS engine at runtime in some configurations. The label must be set using a Script node. Fix Add a Script node at the start of your Experience Flow:

// TTS configuration
userSessionUtils.setCallControlParam('ttsProvider', 'google');
userSessionUtils.setCallControlParam('ttsLabel', 'your_google_label');
userSessionUtils.setCallControlParam('ttsLanguage', 'en-US');
userSessionUtils.setCallControlParam('voiceName', 'en-US-Chirp3-HD-Aoede');

// ASR configuration (same approach)
userSessionUtils.setCallControlParam('sttProvider', 'google');
userSessionUtils.setCallControlParam('sttLabel', 'your_google_label');

The Voice Preferences UI setting for ttsLabel doesn’t propagate at runtime for Google TTS. Always set the label using a script.

Background Streaming-Prompts Not Played

Symptom When using Google TTS with Chirp voices and Background Streaming enabled at the Experience Flow level, follow-up prompts (for example, Is there anything else?) are logged as synthesized successfully, but no audio is delivered to the caller. Fix Options

Option	Action
Option 1 — Disable Background Streaming	Go to Flows > Edit > Background Streaming > Disabled. Confirm audio plays correctly.
Option 2 — Use non-Chirp voice	Switch to a Google Neural voice (for example, `en-US-Wavenet-F`). Confirmed compatible with Background Streaming.
Option 3 — Use Deepgram TTS	Deepgram TTS has native streaming support and is more stable with Background Streaming enabled.

Google Chirp voices aren’t compatible with Background Streaming.

Sarvam TTS

TTS Fails on Currency, Commas, Special Characters

Symptom Sarvam TTS fails on messages containing the ₹ symbol, commas in Indian number format (3,40,650.23), or mixed punctuation. The Sarvam API doesn’t handle Unicode currency symbols or Indian number formatting natively. Fix Pre-process text in a Script node before passing to Sarvam TTS:

function sanitizeForSarvam(text) {
  return text
    .replace(/₹([\d,]+\.?\d*)/g, function(m, n) {
      var num = parseFloat(n.replace(/,/g, ''));
      if (num >= 100000) return (num / 100000).toFixed(0) + ' lakh rupaye';
      if (num >= 1000) return (num / 1000).toFixed(0) + ' hazaar rupaye';
      return num + ' rupaye';
    })
    .replace(/[,;]/g, ' ')      // remove commas/semicolons
    .replace(/\.(?!\d)/g, ' ')  // periods NOT followed by a digit
    .replace(/[()\[\]{}]/g, '') // brackets
    .replace(/\s{2,}/g, ' ')    // collapse multiple spaces
    .trim();
}

context.sarvamInput = sanitizeForSarvam(context.llmResponse);

// Provider configuration
userSessionUtils.setCallControlParam('ttsProvider', 'custom:sarvamTTS');
userSessionUtils.setCallControlParam('voiceName', 'simran');

Sarvam doesn’t support SSML tags. Use plain text only. All text formatting must be handled in the sanitization script before passing to Sarvam TTS.

Background Streaming — Audio Not Played

Symptom When Background Streaming is enabled at the Experience Flow level with Sarvam TTS, audio synthesis is logged as successful in Interactions, but zero audio plays to the caller. Fix Disable Background Streaming when using Sarvam TTS. Steps: Go to Experience Flow > Edit > Background Streaming > Disabled.

Sarvam TTS isn’t compatible with Background Streaming. A platform fix is being tracked. Until resolved, Background Streaming must be disabled for any flow using Sarvam TTS. No configuration workaround is available.

Call Flow — Barge-in, Transfer & Transcription

Aggressive Barge-in — Bot Interrupts User Mid-Speech

Symptom The bot plays filler music in under 3 seconds, interrupting the user’s utterance. The ActionHookDelayProcessor fires too early, especially when there is a language mismatch between TTS text and the voice configuration. Fix

// Disable barge-in for specific nodes (legal, OTP, transfer messages)
userSessionUtils.setCallControlParam('node.bargein', false);

// Delay filler music — prevent premature firing
userSessionUtils.setCallControlParam('actionHookDelayInMs', 8000);

// Session-level barge-in sensitivity
userSessionUtils.setCallControlParam('session.bargein', true);
userSessionUtils.setCallControlParam('session.bargeInSensitivity', 'low');

Scenario Guide

Scenario	Recommended Fix
Bot interrupts greetings	Set `actionHookDelayInMs=8000` or higher. Give the caller time to state full intent.
Barge-in on legal/OTP prompts	Set `node.bargein=false` on those specific message nodes.
Background noise triggers barge-in	Set `bargeInSensitivity=low`. Ensure noise suppression is active in your telephony setup.
Transfer message cut short	Use `queueCommand: true` — see Transfer Fires Before TTS Message Completes.

Transfer Fires Before TTS Message Completes

Symptom Agent transfer fires before the farewell or transfer message finishes playing. The caller hears a partial message before being transferred. Root Cause A direct Agent Transfer node placed after a Message node creates a race condition — the transfer may execute before TTS playback completes. Fix Use queueCommand: true in a Script node to ensure TTS completes before transfer executes:

var transferCmd = {
  type: 'command',
  command: 'redirect',
  queueCommand: true, // Critical parameter — waits for TTS to complete
  data: [{
    verb: 'transfer',
    destination: 'sip:agent@your-sip-domain.com'
  }]
};

// Execute using your flow's command execution method.

The queueCommand: true parameter instructs the Voice Gateway to wait until current TTS playback is fully complete before executing the transfer. Use this for any action that follows a voice prompt.

For additional information, refer to Utility Functions in Voice Gateway

Transcription Not Displaying (Twilio / ASR Timeout)

Symptom The Transcripts tab is empty after completed calls. Diagnosis & Fix

Root Cause	How to Diagnose	Fix
Twilio webhook misconfiguration	The Twilio Media Stream webhook is missing the transcript endpoint. Transcription is enabled but results aren’t sent to the platform.	Verify your webhook URL in the Twilio Console includes the correct transcript endpoint for your environment. Contact Support to confirm the correct URL.
ASR timeout too short	ASR timeout is set too low (for example, 2000 ms). Calls end with `NO_INPUT` before transcription completes.	Increase `botNoInputTimeoutMS` to `8000` ms or higher. Verify `sttMinConfidence` isn’t set too high.
ChannelOverrideTemplate override	Template-level timeout silently overrides the Experience Flow settings.	Set `botNoInputTimeoutMS` explicitly in the `ChannelOverrideTemplate` itself, not just in the Flow.

Quick Reference-All Parameters

All parameters are set using userSessionUtils.setCallControlParam('paramName', value).

Parameter	Vendor	Value Range	Purpose
`AzureSegmentationSilenceTimeoutMs`	Azure ASR	800-2000 ms	End-of-speech silence. Increase to reduce utterance splits.
`continuousASR`	Azure, Google	`true/false`	Enable for digit/number collection.
`continuousASRTimeoutInMS`	Azure, Google	1500-3000 ms	Wait after last input in continuous mode.
`sttMinConfidence`	All vendors	0.4-0.7	Minimum confidence to accept an utterance. Lower = more permissive.
`sttLanguage`	All vendors	BCP-47 code	ASR language (for example, `en-US`, `hi-IN`, `es-419`, `ja-JP`).
`sttProvider`	All vendors	Provider string	ASR vendor selection (`microsoft`, `google`, `deepgram`).
`botNoInputTimeoutMS`	All vendors	5000-15000 ms	Wait before no-input event. Increase for non-English callers.
`deepgramUtteranceEndMs`	Deepgram	800-2000 ms	End-of-utterance silence. Pair with `deepgramEndpointing`.
`deepgramEndpointing`	Deepgram	300-600 ms	VAD sensitivity. 300 ms = responsive; 600 ms = patient.
`deepgramNumerals`	Deepgram	`true/false`	Spoken numbers → digits (five hundred → `500`).
`deepgramPunctuate`	Deepgram	`true/false`	Adds punctuation to transcripts.
`deepgramKeyterms`	Deepgram	String array	Boost recognition of specific words.
`ttsLanguage`	All TTS	BCP-47 code	TTS language. Must match `voiceName` language.
`ttsProvider`	All TTS	Provider string	TTS vendor (`microsoft`, `google`, `custom:sarvamTTS`).
`ttsLabel`	Google TTS	Label string	Must be set using a script, not UI only.
`voiceName`	All TTS	Voice identifier	Must match `ttsLanguage`.
`node.bargein`	All vendors	`true/false`	Enable/disable barge-in per node.
`session.bargeInSensitivity`	All vendors	`low/medium/high`	Session-level barge-in sensitivity.
`actionHookDelayInMs`	All vendors	5000-10000 ms	Delay before filler music fires.
`dtmfCollectMaxDigits`	All vendors	1-20	Max DTMF digits to collect (card numbers: `16`).
`dtmfCollectInterDigitTimeoutMS`	All vendors	3000-5000 ms	Wait between DTMF digits.
`dtmfCollectTermDigit`	All vendors	`#` or `*`	Termination digit for DTMF collection.
`listenDuringPrompt`	All vendors	`true/false`	Allow ASR to listen while TTS plays.

​Microsoft Azure ASR

​Utterance Splitting — Speech Split into Multiple Parts

​Digit and Number Misinterpretation

​Widespread ASR Failure-Outage or Concurrency Breach

​Microsoft Azure TTS

​Mispronounced Words-SSML Phoneme and Say-As Tags

​TTS Reading Newlines from LLM or Agentic Responses

​Hindi TTS Failure-Voice-Language Mismatch

​Configured Voice Not Applying

​Deepgram ASR / TTS

​Utterance Splitting — utteranceEndMs Tuning

​Digit and RX Number Recognition

​Google ASR & TTS

​16-Digit Card Number — Repeated Digit Failure

​No Credentials for Google

​Background Streaming-Prompts Not Played

​Sarvam TTS

​TTS Fails on Currency, Commas, Special Characters

​Background Streaming — Audio Not Played

​Call Flow — Barge-in, Transfer & Transcription

​Aggressive Barge-in — Bot Interrupts User Mid-Speech

​Transfer Fires Before TTS Message Completes

​Transcription Not Displaying (Twilio / ASR Timeout)

​Quick Reference-All Parameters

Microsoft Azure ASR

Utterance Splitting — Speech Split into Multiple Parts

Digit and Number Misinterpretation

Widespread ASR Failure-Outage or Concurrency Breach

Microsoft Azure TTS

Mispronounced Words-SSML Phoneme and Say-As Tags

TTS Reading Newlines from LLM or Agentic Responses

Hindi TTS Failure-Voice-Language Mismatch

Configured Voice Not Applying

Deepgram ASR / TTS

Utterance Splitting — utteranceEndMs Tuning

Digit and RX Number Recognition

Google ASR & TTS

16-Digit Card Number — Repeated Digit Failure

No Credentials for Google

Background Streaming-Prompts Not Played

Sarvam TTS

TTS Fails on Currency, Commas, Special Characters

Background Streaming — Audio Not Played

Call Flow — Barge-in, Transfer & Transcription

Aggressive Barge-in — Bot Interrupts User Mid-Speech

Transfer Fires Before TTS Message Completes

Transcription Not Displaying (Twilio / ASR Timeout)

Quick Reference-All Parameters