Arduino Voice Recognition: LD3320 and EasyVR Module Guide

Adding an Arduino voice recognition module to your project opens up hands-free control that feels genuinely futuristic — say “lights on” and LEDs light up, say “temperature” and the reading appears on your display. Unlike cloud-based voice assistants, embedded voice recognition modules like the LD3320 and EasyVR work completely offline, with no internet connection, no latency, and no privacy concerns. This guide compares both modules in depth, walks you through wiring and training, and shows you how to build a practical voice-controlled system from scratch.

Table of Contents

• 1. How Embedded Voice Recognition Works
• 2. LD3320 Module: Speaker-Independent ASR
• 3. EasyVR Module: Speaker-Dependent Recognition
• 4. LD3320 vs EasyVR: Full Comparison
• 5. LD3320 Wiring and Arduino Code
• 6. EasyVR Wiring, Training, and Code
• 7. Voice-Controlled Home Automation Project
• 8. Tips for Better Recognition Accuracy
• FAQ

1. How Embedded Voice Recognition Works

Modern embedded voice recognition does not transcribe speech to text in real time — that would require far more processing power than an Arduino can provide. Instead, these modules use one of two approaches:

Speaker-Independent ASR (Automatic Speech Recognition): The module ships with pre-trained acoustic models for a fixed vocabulary. It compares incoming audio to statistical models and outputs the closest match. The LD3320 uses this approach — you define keywords in its firmware and it recognises them regardless of who speaks them, in any accent.

Speaker-Dependent Template Matching: The user speaks each command several times during a training phase. The module records the spectral “fingerprint” of your voice speaking each command. During recognition, incoming audio is compared to these stored fingerprints. The EasyVR uses this approach — it recognises the specific person who trained it best, but can be trained for any language and any words.

Both approaches are fundamentally different from what Google Assistant or Alexa do. They work on vocabularies of 10–200 words, not open-ended conversation. But for home automation, robot control, or device management, a 20-word vocabulary covers virtually every command you need.

Key concepts:

• False acceptance rate (FAR): How often the module incorrectly accepts a non-command as a match. Lower is better.
• False rejection rate (FRR): How often the module misses a correctly spoken command. Lower is better.
• Trigger word / wake word: A special command that activates the module before subsequent commands are processed. Prevents false activations from background conversation.

2. LD3320 Module: Speaker-Independent ASR

The LD3320 is a single-chip ASR processor developed by ICroute. The Arduino-compatible module version comes with an onboard microphone, SPI or UART interface, and a straightforward library. It is one of the most accessible voice recognition chips for embedded use.

Key specifications:

• Interface: SPI (primary), UART on some variants
• Supply voltage: 3.3V (some modules have onboard regulator for 5V input)
• Microphone: Onboard electret with adjustable gain
• Vocabulary: Up to 50 keywords (firmware-defined, not user-trained)
• Language support: Chinese, English (word-level, no connected speech)
• Recognition distance: 1–3 metres in quiet environment
• Response time: ~100–300 ms after word completion
• MP3 playback: Some LD3320 module variants also support audio output

How to define keywords: Keywords are set in the Arduino sketch via the library. The LD3320 uses a phoneme-based system — you provide the keyword string and the chip converts it to phoneme sequences internally. For English, you type the word as-is. Recognition is speaker-independent, so anyone saying the trained word triggers a match.

Advantages: Speaker-independent (no training needed), anyone can use it, relatively easy setup, inexpensive.

Limitations: Limited to its supported phoneme set (some accented English words recognise poorly), SPI wiring is slightly complex, 3.3V logic, firmware-dependent vocabulary (cannot train arbitrary sounds), Chinese-focused architecture means English accuracy is somewhat lower.

3. EasyVR Module: Speaker-Dependent Recognition

The EasyVR (developed by Tigal, now in version 3.x) is a dedicated voice recognition module that communicates with Arduino via UART. It comes with the EasyVR Commander desktop software for training and management, and a comprehensive Arduino library.

Key specifications:

• Interface: UART at 9600 baud (SoftwareSerial compatible)
• Supply voltage: 5V
• Microphone: External required (3.5mm jack) — module does not include a mic
• Vocabulary: Up to 32 custom speaker-dependent commands per group, 5 groups = 160 total
• Built-in speaker-independent commands: 25 fixed trigger words (“Robot”, “Action”, “Move”, “Turn”, etc.) in Group 0
• Language support: Language-agnostic (train any word in any language)
• Training: 5 repetitions per command recommended
• Recognition confidence: Returns confidence level with each match
• Response time: 300–800 ms

EasyVR Commander software: Free Windows/Mac/Linux app that connects to EasyVR via Arduino and guides you through training. Visual interface for managing command groups, testing recognition, and exporting. You can train commands without writing any code.

Advantages: 5V UART (easy Arduino connection), language-agnostic (train in Hindi, Tamil, English — anything), confidence scores for threshold-based acceptance, built-in speaker-independent trigger words in Group 0, well-supported library.

Limitations: Speaker-dependent (trained for specific speaker, degrades for others), requires external microphone, requires training session, EasyVR 3 modules are more expensive than LD3320.

4. LD3320 vs EasyVR: Full Comparison

Verdict: For projects used by one person (personal robot, home automation for one user), EasyVR’s speaker-dependent training gives higher accuracy. For public installations (museum kiosk, classroom project, multi-user system), LD3320’s speaker-independent approach is essential. For Indian language commands, EasyVR wins — LD3320’s phoneme engine is not optimised for Hindi, Tamil, or other Indic languages.

5. LD3320 Wiring and Arduino Code

LD3320 Wiring (Uno):

• LD3320 VCC → 3.3V
• LD3320 GND → GND
• LD3320 CS → Arduino Pin 10 (SPI SS)
• LD3320 SCK → Arduino Pin 13 (SPI SCK)
• LD3320 MOSI → Arduino Pin 11 (SPI MOSI)
• LD3320 MISO → Arduino Pin 12 (SPI MISO)
• LD3320 WR/IRQ → Arduino Pin 2 (interrupt)
• LD3320 RST → Arduino Pin 8

Use a 3.3V level shifter on the SPI lines if your LD3320 module is not 5V-tolerant. Many breakout boards include onboard level shifting.

Install library: Search Library Manager for LD3320 or install from GitHub (HopeBaron/LD3320-Lib).

#include <LD3320.h>

#define LD_CS  10
#define LD_RST 8
#define LD_IRQ 2

LD3320 asr(LD_CS, LD_RST, LD_IRQ);

// Define keywords (up to 50)
const char* keywords[] = {
  "LIGHTS ON",
  "LIGHTS OFF",
  "FAN ON",
  "FAN OFF",
  "TEMPERATURE"
};
const int KEYWORD_COUNT = 5;

void onRecognized(uint8_t index) {
  Serial.print("Recognized: ");
  Serial.println(keywords[index]);
  
  switch (index) {
    case 0: digitalWrite(4, HIGH); break; // Lights on
    case 1: digitalWrite(4, LOW);  break; // Lights off
    case 2: digitalWrite(5, HIGH); break; // Fan on
    case 3: digitalWrite(5, LOW);  break; // Fan off
    case 4: /* Read and display temperature */ break;
  }
}

void setup() {
  Serial.begin(9600);
  pinMode(4, OUTPUT); // Lights relay
  pinMode(5, OUTPUT); // Fan relay
  
  asr.begin();
  for (int i = 0; i < KEYWORD_COUNT; i++) {
    asr.addKeyword(i, keywords[i]);
  }
  asr.setCallback(onRecognized);
  asr.startRecognition();
  
  Serial.println("LD3320 listening...");
}

void loop() {
  asr.run(); // Non-blocking recognition loop
}

Note: The exact API varies by library version — check the library examples included with your installation.

6. EasyVR Wiring, Training, and Code

EasyVR Wiring (Uno):

• EasyVR VCC → 5V
• EasyVR GND → GND
• EasyVR TX → Arduino Pin 12 (SoftwareSerial RX)
• EasyVR RX → Arduino Pin 13 (SoftwareSerial TX)
• EasyVR MIC+ and MIC- → Electret microphone (with 10kΩ bias resistor)

Training with EasyVR Commander:

1. Download and install EasyVR Commander from the official site
2. Connect EasyVR to Arduino with the above wiring; connect Arduino via USB
3. Switch EasyVR to “Commander Mode” using the mode jumper
4. In EasyVR Commander, select your COM port and connect
5. Create a new group (e.g., Group 1) and add commands: “ACTIVATE”, “LIGHTS ON”, “LIGHTS OFF”, etc.
6. Click Train for each command — speak it clearly 5 times when prompted
7. Test using the Test button — green = recognised correctly
8. Switch EasyVR back to normal mode before running your sketch

#include <SoftwareSerial.h>
#include <EasyVR.h>

SoftwareSerial easyvrSerial(12, 13); // RX, TX
EasyVR easyvr(easyvrSerial);

#define GROUP_COMMANDS 1

// Must match training order in EasyVR Commander
const char* commands[] = {
  "ACTIVATE",
  "LIGHTS ON",
  "LIGHTS OFF",
  "FAN ON",
  "FAN OFF"
};

void setup() {
  Serial.begin(9600);
  easyvrSerial.begin(9600);
  
  if (!easyvr.detect()) {
    Serial.println("EasyVR not found. Check wiring.");
    while (true);
  }
  
  easyvr.setPinOutput(EasyVR::IO1, LOW);
  Serial.println("EasyVR ready. Say trigger word first.");
  
  // Start listening for trigger word (Group 0, SI commands)
  easyvr.recognizeCommand(0);
}

void loop() {
  if (!easyvr.hasFinished()) return;
  
  int index = easyvr.getWord(); // Group 0: SI trigger words
  
  if (index >= 0) {
    // Trigger word detected — now listen for our commands
    Serial.println("Trigger heard. Listening for command...");
    easyvr.recognizeCommand(GROUP_COMMANDS);
    
    while (!easyvr.hasFinished());
    
    int cmd = easyvr.getWord();
    if (cmd >= 0) {
      Serial.print("Command: ");
      Serial.println(commands[cmd]);
      
      switch (cmd) {
        case 1: digitalWrite(4, HIGH); break; // Lights ON
        case 2: digitalWrite(4, LOW);  break; // Lights OFF
        case 3: digitalWrite(5, HIGH); break; // Fan ON
        case 4: digitalWrite(5, LOW);  break; // Fan OFF
      }
    } else {
      Serial.println("Command not recognised");
    }
  }
  
  // Go back to listening for trigger
  easyvr.recognizeCommand(0);
}

7. Voice-Controlled Home Automation Project

Here is a complete project concept combining the EasyVR with relay control for 3 home appliances:

Hardware list:

• Arduino Uno
• EasyVR module + electret microphone
• 4-channel relay module (5V coil, 230V rated contacts)
• 16×2 LCD with I2C backpack
• Buzzer for audio feedback

Commands to train (Group 1): “LIGHTS ON”, “LIGHTS OFF”, “FAN ON”, “FAN OFF”, “AC ON”, “AC OFF”, “ALL OFF”

Trigger word: Use the built-in “ROBOT” or “ACTION” from Group 0 (speaker-independent) — this means anyone can activate the system even if only one person trained the control commands.

System behaviour:

1. System waits silently for trigger word
2. On trigger: LCD shows “Listening…”, buzzer beeps once
3. User says command within 5 seconds
4. On recognition: relay switches, LCD shows confirmation, buzzer beeps twice
5. On failure: LCD shows “Retry”, single long beep

The 5-second recognition window in EasyVR can be configured via the timeout parameter. For public installations, consider setting a 3-second window to prevent false activations from long background conversations.

8. Tips for Better Recognition Accuracy

• Train in the deployment environment: Acoustics in your room affect recognition significantly. Train with the module mounted where it will actually be used, not on a desk during development.
• Microphone placement: Place the microphone 20–50 cm from the speaker’s position. Too close causes clipping; too far reduces SNR. Avoid placement near fans or air conditioning vents.
• Keyword design: Choose commands with distinct phoneme patterns. “FAN” and “VAN” may confuse the system; “FAN ON” and “DISABLE” will not. Longer commands (2+ syllables) generally recognise better than single syllables.
• Reduce background noise: Both modules degrade significantly with HVAC noise, music, or TV audio in the background. Add a hardware noise gate (VOX circuit) upstream of the microphone input if needed.
• EasyVR: train multiple speakers: EasyVR supports multiple speaker sets. Train the same commands for all family members to improve multi-user acceptance rates.
• Adjust confidence threshold: EasyVR’s confidence score (0–100) can be used to filter dubious matches. Accept only commands with confidence > 50 to reduce false positives.
• Recharge/retrain periodically: EasyVR’s template memory can drift over time, especially with changes in the environment. Re-train every few months for stable long-term accuracy.

Frequently Asked Questions

Can these modules understand complete sentences?

No — both the LD3320 and EasyVR are keyword/command spotters, not full speech recognition engines. They compare incoming audio to a fixed vocabulary of trained words or phrases. Commands like “turn the lights on in the bedroom” need to be simplified to single phrases like “BEDROOM ON” for reliable recognition.

Can I use Hindi or other Indian language commands with EasyVR?

Yes. EasyVR’s speaker-dependent training is completely language-agnostic — it records acoustic patterns, not phoneme models. Train commands like “BATTI JALO” (lights on) or “PANKHA BAND” (fan off) by speaking them 5 times during training, and they work just as reliably as English commands.

What is the recognition range with a standard electret microphone?

With a standard 6mm electret capsule and 10kΩ bias resistor, reliable recognition range is 0.5–1.5 metres in a quiet environment. For larger rooms, use a directional condenser microphone or add a microphone amplifier circuit (MAX9814 auto-gain module works well).

Can I use the Arduino Nano 33 BLE Sense instead of a dedicated module?

Yes — the Nano 33 BLE Sense has an onboard MP34DT05 digital MEMS microphone and runs TensorFlow Lite Micro for edge inferencing. With Arduino’s Edge Impulse integration, you can train a completely custom keyword detection model. This approach is more powerful but requires more setup work than plug-in modules.

My EasyVR returns random recognitions even with no speech. What is wrong?

Background noise is triggering false positives. Solutions: increase the recognition confidence threshold, shorten the listening window, add a noise gate, or move the microphone away from fans and air vents. Also check that your electret microphone is correctly biased (10kΩ pull-up to VCC required) — an unbiased microphone picks up electrical noise as if it were audio.

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